Abstract
ABSTRACT
Client or service user perspectives are important when designing curricula for professional programs. In the case of veterinary technology, an emerging profession in the veterinary field in Australasia, client views on desirable graduate attributes, skills, and knowledge have not yet been explored. This study reports on a survey of 441 veterinary clients (with 104 responses) from four veterinary practices in Brisbane, Queensland, conducted between October 2008 and February 2009. The included veterinary practices provided clinical placements for veterinary technology undergraduates and employment for veterinary technology graduates (2003–2007). Client socio-demographic data along with ratings of the importance of a range of technical (veterinary nursing) skills, emotional intelligence, and professional attributes for veterinary technology graduates were collected and analyzed. Overall, the majority of clients viewed technical skills, emotional intelligence, and professional attributes as important in the clinical practice of veterinary technology graduates with whom they interacted in the veterinary practice. Client interviews (n=3) contextualized the survey data and also showed that clients attached importance to graduates demonstrating professional competence. Agglomerative hierarchical cluster analysis revealed four distinct groupings of clients within the data based on their differing perceptions. Using a multivariable proportional-odds regression model, it was also found that some client differences were influenced by demographic factors such as gender, age, and number of visits annually. For example, the odds of female clients valuing emotionality and sociability were greater than males. These findings provide useful data for the design of a professionalizing and market-driven veterinary technology curriculum.
Veterinary technology is an emerging profession in the veterinary and allied animal health fields in Australasia. Its story as an emerging profession, or semi-profession, is comparable to the human health professions, evolving as a relatively new discipline in higher education with an applied or clinical science framework,1 and not yet having met all the criteria of a profession.2 Veterinary technology programs in Australasia currently include a three-year Bachelor of Applied Science (Veterinary Technology) offered by the University of Queensland (UQ), Australia, and a similar Bachelor of Veterinary Technology degree offered by New Zealand's Massey University. Japan's Nippon Veterinary and Life Science University (NVLU) delivers a four-year Bachelor of Veterinary Nursing and Technology, while Kasetsart University, Bangkok, Thailand delivers a four-year Bachelor of Science (Veterinary Technology).
In Australia and New Zealand, veterinary technology has been described as a mid-tier qualification filling a niche between veterinary science and the vocationally based veterinary nursing.3 It provides a higher education pathway into a veterinary nursing career, with veterinary nursing in general and specialist veterinary practices being the largest employment sector for UQ veterinary technology graduates. However, the graduate employment destinations are broader than veterinary nursing and include veterinary practice management, wildlife hospitals and zoos, animal behavior management, animal welfare agencies, veterinary diagnostic laboratories, biosecurity inspection in government agencies, large animal health, animal health technology (research), veterinary pharmaceutical and nutrition companies, and veterinary nurse education (vocational and higher education sectors).4 Graduates from Massey University,5 Kasetsart University, and NVLU experience similar career opportunities.6,7
It should be noted that the titles adopted for veterinary support personnel differ among countries, for example, veterinary nurse, veterinary technician, and veterinary technologist. The American Veterinary Medical Association (AVMA) describes the veterinary technician as “a graduate of a two- or three-year AVMA accredited program in veterinary technology” and a veterinary technologist as “a graduate of a four-year AVMA accredited program in veterinary technology.”8 In Australia and the UK, veterinary nurse is the title used for veterinary support personnel with a nationally recognized vocational qualification in veterinary nursing.9 It would therefore seem reasonable to say that veterinary nursing in Australia and the UK is equivalent to veterinary technology in the US, albeit with some differences in educational and licensing requirements. The same could be said for the higher education programs in veterinary nursing in the UK and veterinary technology in the US and Australasia. UQ, Massey University (New Zealand), and NVLU (Japan) chose to use veterinary technology for their higher education programs to reflect the more diverse curriculum and graduate employment outcomes. As described by the Royal College of Veterinary Surgeons in the UK, the higher education programs are longer in duration, have an academic focus, and provide additional career opportunities in research and the veterinary industries as well as clinical practice.10
In training professional graduates for the twenty-first century, curriculum designers could consider socio-politically driven, international trends in the more recently professionalized health professions where users have been involved in curriculum planning, teaching, and evaluation of professional programs.11–15 These developments are also reflected in the delivery of more patient-centered medical services where democratic professionalism, or giving a voice to consumers and service users, has been advocated.16 Thus, to meet current challenges of changing societal needs and economic sustainability,17–20 veterinary technology curriculum designers could similarly consider the needs of veterinary clients or service users,21–23 as key stakeholders in veterinary technology education and practice.
Veterinary technology practice involves servicing clients through the provision of high level technical support in areas such as surgical nursing, imaging, and clinical pathology, as well as delivering nurse consultations.24–26 Research into the veterinary consultation process shows that communication and relational skills are vital for graduates.27,28 Hence, similar to other professional programs, veterinary technology curricula require a holistic approach to professional competence focusing not only on discipline-specific knowledge, problem solving, and technical skills, but also on engaging the affective domain relating to attitudes, values, and emotions.29 In this regard, veterinary technology curriculum designers can learn from human nursing, where emotional intelligence (EI) training is advocated to develop emotional competence,13,30 an imperative for producing graduates who can relate to and communicate with clients.
The construct of EI has generated much debate in the literature, with its use reported in educational, organizational, and clinical settings.31 Several prominent models have evolved and include32 (1) the Mayer & Salovey ability-based model, which views EI as a form of intelligence or cognitive ability (2001);33 (2) the Bar-On emotional-social intelligence (ESI) model based on non-cognitive, emotional, and social competencies (1997);34 and (3) the Goleman mixed model based on a set of emotional competencies and personality traits (1995).35 A fourth, more recent model, the Trait EI model, views EI as a personality trait rather than a cognitive ability, basing this on the subjective nature of emotional experience.31,36 This subjectivity has been cited as a reason for questioning the use of Ability EI models compared to Trait EI, which “can provide a scientifically viable context for the ever-growing number of … intelligences (interpersonal, intrapersonal, emotional, social, etc).”31 The Trait EI framework includes four broad overarching EI factors (well-being, self-control, emotionality, and sociability) and 15 core emotion-related items or facets linked to personality dispositions and self-perceptions of emotional abilities.31,36 Two of the 15 items, adaptability and self-motivation, are not associated with any of the four factors.31,36 The breadth of the factors and core EI items in the Trait EI model and their construct validity make them suitable for use in a study of veterinary client perceptions of EI attributes in veterinary technology graduates.
Currently, there is a paucity of research into veterinary client perspectives on the desirable skills and attributes of veterinary support personnel. The aims of this study were to elucidate veterinary clients' (service users') views on the importance of a range of technical skills, EI, and professional attributes of Australian veterinary technology graduates working in a clinical environment. The association between these views and client demographics was also explored, with considerations of how to shape a market-driven curriculum for an emerging profession.
This study was one of a series of five studies involving key stakeholder groups of the UQ Bachelor of Applied Science (Veterinary Technology). The study was conducted between October 2008 and February 2009, with participating clients sourced from four urban veterinary practices in southeast Queensland. The veterinary practices were a convenience sample of those who were accessible, willing to participate, and had employed a minimum of two veterinary technology graduates (at least one employed for a minimum of two years) and provided clinical placements for a minimum of two final-year veterinary technology students during the period 2003–2007. The practices were one small-animal general practice, one mixed small-animal and equine general practice, one small-animal veterinary teaching and referral hospital, and one small-animal specialist referral hospital.
The study used an explanatory, sequential mixed methods research design involving two phases. Phase one involved collecting and analyzing quantitative data (a questionnaire), then building on these results with qualitative data (interviews) to increase the validity of findings and provide a more comprehensive picture of client perspectives.37 In a second phase, quantitative data were analyzed using agglomerative hierarchical cluster analysis and multivariable proportional-odds regression models to further examine client perceptions in light of socio-demographic factors.
A systematic random sampling method was used to select participants. Figure 1 describes the process of client selection, exclusion, and responses. The client number from each practice was proportional to the size of its client base during the study period. With an anticipated response rate of 50%, sample size calculations indicated that 220 responses from the 441 participants would be required to undertake the planned statistical analysis.
Figure 1: Flow diagram for client selection, exclusion, and response numbers
All data collection for this study was approved through the ethical review process for human ethics at the University of Queensland, Australia. The approval process complied with guidelines made in accordance with the National Health and Medical Research Council Act 1992.38
Data collection primarily involved a purpose-designed questionnaire, the Veterinary Technologist Questionnaire—The Clients' Perspective, comprising 60 questions to draw out the background of clients and their expectations regarding the technical skills, EI, and professional attributes of veterinary technologists working in a veterinary practice.
Part One of the questionnaire sought background information on the clients, including six socio-demographic items (age, gender, number of years as a client at a primary care veterinary practice, number of visits annually, the species of animals taken to the veterinary practice, and location of the primary care veterinary practice [rural, urban, or regional]).
In Part Two, clients responded to 42 five-point Likert-type questions (1=very unimportant to 5=very important) (Table 1). Six questions were negatively worded to reduce acquiescent response bias.39 Thirty-two questions involved EI attributes based on 14 of the 15 EI items described in the Trait EI model.40 One of the EI items based on personal relationships was excluded as it was not relevant to the client context. The remaining 10 questions were about professional attributes associated with two professional factors, teamwork and reflection.
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Table 1: EI and professional attributes from Part Two of the client questionnaire
| EI factors | EI items | EI attributes |
|---|---|---|
| Emotionality | Empathy | 1. Can understand others' needs |
| 2. Can put themselves in someone else's shoes | ||
| 3. Fluent in communicating emotions | ||
| Emotion expression | 4. Able to express their feelings accurately to others | |
| Emotion perception | 5. Able to interpret emotional signals from others | |
| 6. Clear about what they feel | ||
| Sociability | Social awareness | 7. Socially sensitive and perceptive |
| 8. Excellent social skills | ||
| Emotion management | 9. Able to make others feel better | |
| 10. Can positively influence others' feelings (e.g., motivate them) | ||
| Assertiveness | 11. Able to confront others when necessary | |
| 12. Able to ask for what they want | ||
| 13. Always defer to the veterinarian (negative attribute) | ||
| 14. Non-assertive (negative attribute) | ||
| Well-being | Happiness | 15. Cheerful |
| Optimism | 16. Optimistic | |
| 17. Expect positive things to happen in life | ||
| Self-esteem | 18. Self-confident | |
| 19. Have difficulty accepting compliments (negative attribute) | ||
| Self-control | Stress management | 20. Can handle pressure calmly and effectively |
| 21. Well-developed coping mechanisms | ||
| Emotion regulation | 22. Able to control emotions | |
| 23. Able to alter their moods through personal insight and effort | ||
| Impulsivity (low) | 24. Think before they act | |
| 25. Reflect carefully before making decisions | ||
| 26. Impulsive (negative attribute) | ||
| Adaptability* | 27. Flexible in approach to work | |
| 28. Adapt well to change | ||
| Self-motivation* | 29. Self-motivated | |
| 30. Determined and persevering | ||
| 31. Lacking drive and persistence (negative attribute) | ||
| 32. Motivated mainly by rewards (negative attribute) |
| Professional factors | Professional attributes | |
|---|---|---|
| Team work | 1. Cooperative | |
| 2. Conscientious | ||
| 3. Accepting of others | ||
| 4. Share their professional values with colleagues | ||
| 5. Agreeable | ||
| Reflection | 6. Receptive to guidance | |
| 7. Learn from their mistakes | ||
| 8. Know their own limitations | ||
| 9. Open-minded | ||
| 10. Able to critically analyze situations and experiences | ||
*EI items not categorized under four EI factors
Part Three comprised 10 five-point Likert-type questions (using the same rating scale) based on technical (veterinary nursing) skills of the veterinary technologist (Table 2). These technical skills were based on a review of advanced training for veterinary technicians in North America.41 Part Four investigated client perceptions of the importance of teamwork for veterinary technologists via a single five-point Likert-type question and one open-ended question seeking further comments about personal attributes and teamwork.
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Table 2: Technical skills assessed in Part Three of the client questionnaire
| Technical skills | Items |
|---|---|
| Clinical skills | 1. Prioritize patients according to illness or injury |
| 2. Take blood samples | |
| 3. Perform preliminary examination of animal on admission | |
| 4. Assist with physical therapy techniques for the veterinary patient | |
| 5. Take X-rays | |
| Client education | 6. Provide advice on animal care (e.g., worming, vaccination, desexing) |
| 7. Provide advice on dental health and hygiene for animals | |
| 8. Provide appropriate nutritional advice for animals | |
| 9. Conduct puppy pre-school classes | |
| 10. Conduct obesity clinics for cats and dogs |
Pilot testing of the questionnaire was conducted with veterinary clients (n=2), veterinarians (n=2), a veterinary nurse (n=1), and veterinary academics (n=2). Revisions were made based on feedback from this group.
The interviews involved a convenience sample of three clients from two of the four participating veterinary practices. The interviewees (1) were identified as key informants by the practices (based on frequency of visits and the level of bonding with their animals), (2) provided maximum variation in client characteristics (one client was a dog breeder, another kept dogs for recreational purposes, and the third had a dog exclusively for companionship; all had multiple pets), and (3) represented both genders.
One week before the interviews, clients received a copy of the interview guide, comprising seven questions exploring client characteristics, the level of client interaction with the veterinary practice and a veterinary technologist, and client views on EI attributes based on questionnaire data. Clients chose to be interviewed either at their home or workplace. Each interview was conducted in an informal manner over approximately 20 minutes, audio-recorded, and electronically transcribed verbatim.
To maintain client confidentiality, the questionnaires were mailed directly to the clients from their veterinary practices along with a letter outlining the objectives of the study, a reply-paid envelope to return the questionnaire, and a letter from the practice endorsing the research study. The questionnaires were mailed twice, approximately three weeks apart, in an attempt to capture non-respondents from the initial mailing.
Questionnaire data were analyzed using descriptive statistics to determine the percentage of clients who rated an item from 1 (very unimportant) to 5 (very important) on the five-point Likert scale. Descriptive data were then used to identify the five EI attributes considered most important by the highest percentage of clients. These attributes formed the basis of the questions for the semi-structured interviews conducted by the first author in January to February 2009 to validate and expand on the questionnaire data analysis.
Continuous questionnaire items such as age and number of visits were described using mean, median, minimum and maximum values, and first and third quartiles, while categorical variables, such as gender, were presented as percentages. Distributions of responses by clients on the Likert scales were initially assessed using frequency plots. Associations between responses on Likert scales and client demographics were assessed using a univariable proportional-odds regression model. Predictor variables such as age and gender (predicted to affect the response of dependent variables) were selected from the univariable analysis (based on a likelihood-ratio test at p≤.20) and then assessed using a backward stepwise model-building procedure and a multivariable proportional-odds regression model until all remaining predictor variables in the model were significantly associated with the attitude (p<.05). The odds ratio (OR), a measure of association used in logistic regression, was calculated by exponentiating the coefficients from the regression models. ORs increase the power to find differences in data by comparing the relative odds of the occurrence of the outcome of interest.42 Thus, ORs were used to estimate the odds of a particular Likert-type rating or higher occurring in one group of a predictor variable relative to a reference group. An OR value of >1 indicates the outcome of interest is more likely to occur in the at-risk group compared to the reference group.43 The 95 percent confidence interval (CI) was used to estimate the precision of the OR.
Proportional-odds (ordinal) regression models assume that the ORs are the same throughout the Likert-type scales categories (the proportional-odds assumption). To test this assumption, the ordinal likelihood-ratios were compared with those from multinomial logistic models with the same set of predictor variables. Where a significant difference was identified between these models using likelihood-ratio tests, a comparison recommended by Faraway,44 the proportional-odds assumption was assumed to be violated, and the proportional-odds model was rejected. Results from the multinomial models are not presented here. Data manipulation and analysis were conducted using Microsoft© Excel 2007 and R 3.0,a,b respectively.
To capture differences and similarities in client responses in rating the skills and attributes of the veterinary technologist, an agglomerative hierarchical cluster analysis was performed. Cluster analysis is a data reduction tool used to organize large quantities of data into meaningful or similar groups, and it reveals associations not previously evident in data.45 Hierarchical agglomerative cluster analysis is a statistical method for finding relatively homogeneous clusters and has been used by business organizations to develop a typology of clients45 and in health professions to shape interventions to suit the profiles of the targeted group.46 In conducting this analysis, the survey item was initially assigned to its own cluster (one item=one cluster). Using an iterative procedure, the agglomerative hierarchical clustering algorithm joined at each stage the two most similar clusters until there was a single cluster. At each stage, squared distances between clusters were recomputed by the Lance–Williams dissimilarity update formula according to the Ward's minimum variance method. Ward's method was selected because clusters were merged based on minimum increase in total within-cluster variance after merging, resulting in compact and spherical clusters.47,48
Prior to transcript analysis, hardcopies of the transcripts were mailed out to clients for member checking. Thematic analysis was then conducted to extract themes relating to EI. This process involved analyzing transcripts using concept-driven coding with the codes or categories based on EI items from the interview guide, thus using a form of framework analysis.49 The analysis comprised two phases. Phase 1 involved immersion in the data to gain an understanding of the content by (1) reading all three transcripts once, and then reading an individual transcript twice again before analyzing it, and (2) underlining keywords/phrases/ideas exemplifying codes and notating initial codes in the margins. Phase 2 involved re-reading and reflecting on the coded data to identify more in-depth meanings and to make any required amendments. In transcribing keywords or phrases of interest some of the surrounding text was kept so that the context was not lost.50 An example of the process is found in Table 3.
|
Table 3: Example of thematic analysis of transcribed interview data
| Key idea/phrase | Code (EI item) | Theme (EI factor) |
|---|---|---|
| “You were a person. Your dogs were known and there was a real interest in them. One of the loveliest people I have ever interacted with.” | Social awareness | Sociability |
From a total of 441 questionnaires administered, excluding six questionnaires that were returned unopened, the response rate was 24% (104/435). A further six clients were removed from the analysis due to multiple missing responses resulting in 98 respondents. Eighty-two percent of clients were female and 18% were male. The primary care veterinary practice location was urban for 87% clients, regional for 12%, and rural for 1%. Sixty-two percent of clients owned dogs, while 38% owned other species, which included cats, horses, birds, and reptiles. Statistics for continuous demographic variables were also determined (Table 4).
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Table 4: Summary statistics for demographic variables of 98 clients participating in the study
| Predictor variable | Frequency (%) | Mean (SD) | Median (Q1, Q3) | Min, Max |
|---|---|---|---|---|
| Client age | 98 (100) | 49 (14) | 49 (37, 62) | 25, 83 |
| Years at primary care veterinary practice | 98 (100) | 7 (7) | 5 (2, 9) | 0, 40 |
| Number of visits annually | 98 (100) | 2 (1) | 2 (2, 2) | 1, 4 |
SD=standard deviation; Q1=first quartile; Q3=third quartile
Overall, the majority of clients viewed 26 of the 32 EI attributes as important or very important with four of the five highest scoring attributes being associated with two EI factors (self-control, emotionality) and one with the EI item of self-motivation (Table 5). More than 83% of clients viewed nine out of the 10 professional attributes (teamwork and reflection) as important or very important (Table 5).
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Table 5: Client percentage ratings of EI and professional attributes of veterinary technology graduates in order of highest ratings of importance per factor or item (adaptability, self-motivation)
| (%) Client rating
|
|||||
|---|---|---|---|---|---|
| Factor | Item | Attributes (rank*) | VU & U | N | VI & I |
| EI | |||||
| Emotionality | 1. Can understand others' needs (1) | 2 | 3 | 95 | |
| 2. Can put themselves in someone else's shoes | 1 | 8 | 91 | ||
| 3. Able to interpret emotional signals from others | 3 | 12 | 85 | ||
| 4. Able to express their feelings accurately to others | 2 | 18 | 80 | ||
| 5. Clear about what they feel | 3 | 23 | 74 | ||
| 6. Fluent in communicating emotions | 7 | 25 | 68 | ||
| Sociability | 7. Able to make others feel better | 0 | 12 | 88 | |
| 8. Able to confront others when necessary | 0 | 12 | 88 | ||
| 9. Socially sensitive and perceptive | 1 | 14 | 85 | ||
| 10. Able to ask for what they want | 2 | 14 | 84 | ||
| 11. Excellent social skills | 4 | 15 | 81 | ||
| 12. Can positively influence others' feelingsa | 4 | 17 | 79 | ||
| 13. Always defer to the veterinarian (negative item)a | 4 | 31 | 65 | ||
| 14. Non-assertive (negative item)a | 18 | 44 | 38 | ||
| Well-being | 15. Self-confident | 0 | 10 | 90 | |
| 16. Cheerful | 0 | 17 | 83 | ||
| 17. Optimistic | 1 | 13 | 86 | ||
| 18. Expect positive things to happen in lifeb | 11 | 32 | 57 | ||
| 19. Have difficulty accepting compliments (negative item)c | 42 | 48 | 10 | ||
| Self-control | 20. Think before they act (2)d | 0 | 0 | 100 | |
| 21. Can handle pressure calmly and effectively (3)d | 2 | 0 | 98 | ||
| 22. Reflect carefully before making decisions (4)d | 0 | 4 | 96 | ||
| 23. Well-developed coping mechanismse | 1 | 20 | 79 | ||
| 24. Able to control emotions | 2 | 20 | 78 | ||
| 25. Able to alter moods through personal insight and effort | 3 | 22 | 75 | ||
| 26. Impulsivef | 49 | 40 | 11 | ||
| Adaptability | 27. Flexible in approach to work | 1 | 8 | 91 | |
| 28. Adapt well to change | 0 | 10 | 90 | ||
| Self-motivation | 29. Self-motivated (5)g | 5 | 0 | 95 | |
| 30. Determined and persevering | 0 | 12 | 88 | ||
| 31. Lacking drive and persistence (negative item)h | 53 | 32 | 15 | ||
| 32. Motivated mainly by rewards (negative item)h | 54 | 41 | 5 | ||
| Professional
|
|
|
|
|
|
| Team work | 1. Cooperativei | 0 | 1 | 99 | |
| 2. Conscientiousi | 1 | 4 | 95 | ||
| 3. Accepting of others | 2 | 8 | 90 | ||
| 4. Share their professional values with colleagues | 2 | 14 | 84 | ||
| 5. Agreeablej | 5 | 30 | 65 | ||
| Reflection | 6. Receptive to guidancek | 0 | 0 | 100 | |
| 7. Learn from their mistakesk | 0 | 3 | 97 | ||
| 8. Know their own limitations | 0 | 5 | 95 | ||
| 9. Open-minded | 0 | 5 | 95 | ||
| 10. Able to critically analyze situations and experiencesk | 0 | 7 | 93 | ||
VU=very unimportant; U=unimportant; N=neutral; I=important; VI=very important
*EI attributes that the highest percentage of clients viewed as important (basis for interview questions) are listed from 1 to 5.
a–h Clusters of EI attributes identified in client groups (see Table 9)
i–k Clusters of professional attributes identified in client groups (see Table 9)
Of the 10 technical skills surveyed, eight were rated as important or very important by at least 88% of clients (Table 6). Around 30% of clients were unsure (3=neutral) about the importance of the remaining two items of conducting obesity clinics and puppy pre-schools.
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Table 6: Client percentage ratings of the importance of technical skills of veterinary technologists in order of highest rating of importance per skill group
| (%) Client rating
|
||||
|---|---|---|---|---|
| Technical Skills | Items | VU & U | N | VI & I |
| Clinical skills | 1. Prioritize patients according to illness or injuryl | 0 | 1 | 99 |
| 2. Take blood samplesl | 0 | 5 | 95 | |
| 3. Perform preliminary examination of animal on admissionl | 2 | 5 | 93 | |
| 4. Assist with physical therapy techniques for the veterinary patientl | 3 | 6 | 91 | |
| 5. Take X-raysl | 2 | 9 | 89 | |
| Client education | 6. Provide advice on animal care (e.g., worming, vaccination, desexing) | 0 | 3 | 97 |
| 7. Provide advice on dental health and hygiene for animals | 0 | 5 | 95 | |
| 8. Provide clients with appropriate nutritional advice for animals | 3 | 9 | 88 | |
| 9. Conduct puppy pre-school classesm | 9 | 34 | 62 | |
| 10. Conduct obesity clinics for cats and dogsm | 7 | 32 | 61 | |
VU=very unimportant; U=unimportant; N=neutral; I=important; VI=very important
l,m Cluster of technical skills identified by client groups (see Table 9)
Socio-demographic variables of age, gender, number of visits to the practice annually, number of years as a client at a practice, and being a dog owner were a significant predictor of association with 12 (10 EI and professional attributes and two technical skills) of the 52 attributes and skills assessed overall. Six attributes/skills did not fit the proportional-odds assumption and were rejected. Results of the multivariate analyses are presented in Tables 7 and 8. The odds of female clients rating emotionality (OR 2.97 95% CI [1.14–7.93]; p=.01) and sociability (OR 2.99 95% CI [1.08–8.57]; p=.03) as important were approximately three times greater than for males. Similarly, the odds of rating teamwork (OR 4.54 95% CI [1.62–13.68]; p<.01) and reflection (OR 4.09 95% CI [1.44-12.73]; p<.01) as important were approximately four times greater for females clients. The odds of older clients rating the flexibility and cheerfulness of the veterinary technologist higher in importance than younger clients were approximately three times greater (Table 7). Table 8 depicts the OR of significant predictors for clients rating the technical skills of veterinary technologists.
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Table 7: Odds ratios (and 95% CIs) of significant predictors for rating a range of EI and professional attributes of the veterinary technologist by 98 clients using a Likert scale (1=very unimportant and 5=very important)
| Factor | Attribute | Predictor* and level |
OR (95% CI) | p† |
|---|---|---|---|---|
| Flexible in approach to work (EI) | Client's age | .01 | ||
| ≤49 years | Ref | |||
| >49 years | 3.74 (1.59–9.38) | |||
| Able to adapt well to change (EI) | Client's age | .02 | ||
| ≤49 years | Ref | |||
| >49 years | 2.61 (1.16–6.47) | |||
| Well-being (EI) | Cheerful | Client's age | .02 | |
| ≤49 years | Ref | |||
| >49 years | 2.61 (1.16–6.14) | |||
| Teamwork | Agreeable | Client's age | .01 | |
| ≤49 years | Ref | |||
| >49 years | 2.58 (1.2–5.68) | |||
| Emotionality (EI) | Fluent in communicating emotions | Gender* | .01 | |
| Male | Ref | |||
| Female | 2.97 (1.14–7.93) | |||
| No. of annual visits | .01 | |||
| 1 | Ref | |||
| 2 | 2.14 (0.79–5.88) | |||
| >2 | 4.24 (1.15–16.14) | |||
| Reflection | Able to critically evaluate situations | Gender | <.01 | |
| Male | Ref | |||
| Female | 4.09 (1.44–12.73) | |||
| No. of annual visits | .02 | |||
| 1 | Ref | |||
| 2 | 2.50 (0.91–7.22) | |||
| >2 | 6.52 (1.67–28.62) | |||
| Emotionality (EI) | Able to express their feelings accurately to others | Gender | .05 | |
| Male | Ref | |||
| Female | 2.68 (1.00–7.33) | |||
| Sociability (EI) | Able to ask for what they want | Gender | .03 | |
| Male | Ref | |||
| Female | 2.99 (1.08–8.57) | |||
| Teamwork | Accepting of others | Gender | <.01 | |
| Male | Ref | |||
| Female | 4.54 (1.62–13.68) | |||
| Pet ownership | <.01 | |||
| Dogs | Ref | |||
| Others | 1.94 (0.86–4.46) | |||
| Teamwork | Cooperative | Gender | <.01 | |
| Male | Ref | |||
| Female | 16.49 (3.01–31.58) | |||
| Regular veterinary practice | .05 | |||
| ≤2 years | Ref | |||
| 3–7 years | 2.9 (1.00–9.39) | |||
| >7 years | 1.46 (0.49–4.47) |
Ref=reference group
*The predictor represents the effect of the predictor variable; odd ratio represents the odds of reporting any particular rating or higher. Interpretation: the odds for rating fluent in communicating emotions at any particular rating or higher increased by 2.97 for female clients compared with male clients (OR 2.97) (95% CI 1.14–7.93).
†Significance of the likelihood-ratio test indicating that a model including the variables improved the fit of the model significantly compared with a reduced or a null model, with an alpha (type I) error equivalent to p.
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Table 8: Odds ratios (and 95% CIs) of significant predictors for rating a range of technical skills of the veterinary technologist by 98 clients using a Likert scale (1=very unimportant and 5=very important).
| Technical skills | Predictor* and level | OR (95% CI) | p† |
|---|---|---|---|
| Conduct puppy pre-school | Years at regular veterinary practice | .05 | |
| ≤2 years | Ref | ||
| 3–7 years | 0.40 (0.15–1.06) | ||
| >7 years | 0.34 (0.13–0.84) | ||
| Gender | <.01 | ||
| Male | Ref | ||
| Female | 8.74 (2.99–28) | ||
| Conduct obesity clinics | No. annual visits | .04 | |
| 1 | Ref | ||
| 2 | 0.29 (0.1–0.76) | ||
| >2 | 0.47 (0.13–1.6) | ||
| Gender | .02 | ||
| Male | Ref | ||
| Female | 6.62 (2.25–21.12) |
Ref=reference group
*The predictor represents the effect of the predictor variable; odd ratio represents the odds of reporting any particular rating or higher. Interpretation: the odds for rating fluent in communicating emotions at any particular rating or higher increased by 2.97 for female clients compared with male clients (OR 2.97) (95% CI 1.14–7.93).
†Significance of the likelihood-ratio test indicating that a model including the variables improved the fit of the model significantly compared with a reduced or a null model, with an alpha (type I) error equivalent to p.
Hierarchical cluster analysis of the questionnaire data revealed four clusters or groupings of clients (highly EI focused; technical skills focused; somewhat EI focused; and negatively EI focused) based on their ratings of particular attributes or skills of the veterinary technologist (Table 9). Clients across these groupings differed in their rating of importance of the EI factors of emotionality (p<.01), sociability (p<.03), and self-motivation (p=.01).
|
Table 9: Comparison of client group (clusters) ratings of technical skills, EI, and professional attributes
| Highly EI focused group (items=26) |
Technical skills focused group (items=15) |
Somewhat EI focused group (items=8) |
Negative EI focused group (items=4) |
||
|---|---|---|---|---|---|
| Factors | %; MR (range) | %; MR (range) | %; MR (range) | %; MR (range) | p* |
| Emotionality | 23; 4 (4–5) | <.01 | |||
| Sociability | 19; 4 (4–5) | 38; 4 (3–4)a | .03 | ||
| Well-being | 12; 4 (4–4) | 13; 4 (3–4)b | 25; 3 (2–3)c | .28 | |
| Self-control | 7; 4 (4–5) | 20; 5 (4–5)d | 13; 4 (4–4)e | 25; 3 (2–3)f | .66 |
| Adaptability | 7; 4 (4–5) | .11 | |||
| Self-motivation | 4; 4 (4–4) | 7; 4 (4–5)g | 50; 2 (2–3)h | .01 | |
| Teamwork | 7; 4 (4–5) | 14; 4 (4–5)i | 13; 4 (4–4)j | .53 | |
| Reflection | 7; 4 (4–5) | 21; 4 (4–5)k | .14 | ||
| Technical skills | 12; 4 (4–5) | 40; 4 (4–5)l | 25; 4 (3–4)m | .07 |
Items=total number of EI and professional attributes or technical skills included in the client groups; %=percentage of EI and professional attributes or technical skills included in the client groups; MR=median ranking of clients' response to factors
*p values were derived using Chi square test
a–h EI attributes identified by client groups (clusters) (Table 5)
i–k Professional attributes identified in client groups in (Table 5)
l,m Technical skills identified by client groups (Table 6)
A summary of the interview results, which further contextualized the five EI attributes identified as important by the highest percentage of clients in the questionnaires, is described in Table 10. These results highlighted the importance of the EI factors identified by the highly EI focused group (emotionality and sociability) and the technical skills focused group (self-control and stress management). The interviewees' views on self-motivation and commitment to professional development also aligned with perceptions of both the technical skills focused and highly EI focused client groups. Three additional themes emerged demonstrating the clients' perceived importance of the veterinary technologist's knowledge, communication skills, and technical competence.
|
Table 10: Summary of themes from interviews of three veterinary clients participating in the study
| Themes | Codes (EI items) (rank*) | Condensed key idea/phrase | Quotation |
|---|---|---|---|
| EI Factors | |||
| Emotionality | Empathy (1) | Being supportive and understanding of the client's situation | The vet nurse was caring and understanding towards us as well. That was a pretty positive experience. |
| Acknowledging the pet as a member of the client's family | … not respecting of the fact that this is someone else's family member. | ||
| Emotion perception (2) | Being aware of the clients' distress and anxiety | … you usually go because of something you're concerned about and there's a level of anxiety and if you're left standing around it just increases that anxiety something phenomenally. | |
| Sociability | Social awareness (3) | Being acknowledged on arrival | Great greeting! Always came out and gave them a pat. It was just this big interaction. Every time we went there, there was just this big smile and wonderful greeting. |
| Having knowledge of the client and their pet; showing interest in them | Just calling us by name. That's reassuring that they know us and we know our dogs are well looked after. | ||
| Emotion management (6) | Settling down clients experiencing stress | They help to settle down everyone else it concerns and the animal settles too. | |
| Self-control | Stress management (4) | Acting calmly and getting on with the job | The staff were really great, just quietly came out … and took me in and settled me down and got a doctor very quickly—a vet very quickly. |
| You want to know they'll act calmly and get on with the job … it is reassuring to me as a customer that the dog is being looked after and he's in safe hands. | |||
| Self-motivation | Intrinsic motivation (5) | Commitment to excellence | Getting some indication that they like their work and like to find out more about their work and are keen to learn and to progress their knowledge base. That's important. |
| Other | |||
| Communication | Flow of information (7) | Keeping clients informed about their pet's condition | So I think there needs to be constant sort of keeping you in touch, being given information. Clear information is also very important. |
| Providing explanations | I like to be informed all the time … I am a scientist by background, so I am naturally curious. | ||
| Knowledge | Source of information (8) | Demonstrating a good knowledge base reassures clients | I guess to me their knowledge is important … because they are the only other person who can tell you what you need to know. |
| Competence | Knowledge and skills (9) | Knowledge and skills reflecting quality patient care | You want to know that the person who's helping to care for your animals knows what they are doing, understands what it is all about, and has demonstrated that knowledge. |
*Ranking of importance for EI items. 1–5 indicate EI items ranked as important in questionnaire by the highest percentage of clients; 6 indicates an additional item that emerged as important in the interviews; 7–9 indicate codes that emerged in the thematic analysis.
In this study, the majority of veterinary client respondents viewed EI and professional attributes to be important in the daily clinical practice of the veterinary technology graduate. The veterinary technologist's work in providing specific advice and performing clinical nursing procedures was also perceived as important by most clients. Four distinct groupings of client views—varying in the importance placed on EI, professional attributes, and technical skills—were identified. Some of these perceptions were affected significantly by client demographics such as age, gender, number of visits to the practice annually, number of years as a client of a practice, and dog ownership, thus reflecting the diversity in the veterinary client population.
The client or service user's pivotal role in professional education has been acknowledged in many fields. This includes the unique and important relationship clients have with professions,51,52 and the importance of professional training and socialization in equipping graduates to service client needs in a rapidly changing world.53,54(p.132) In health and medical education, client input is valued in curriculum planning, teaching, and evaluation of professional programs.11–15 A recent study revealing differences in the views of clients and small-animal veterinarians regarding desirable personal and professional attributes of veterinarians draws attention to the need for further research on veterinary client perspectives to inform veterinary curricula and teaching.55 These arguments and the findings of this study highlight an important research agenda for veterinary technology: seeking client perspectives for the design of innovative, market-driven curricula for an emerging profession.
One of the most important findings from this study was that the majority of clients valued EI in veterinary technology graduates, and that the five EI attributes rated as important by the highest percentage of clients related to EI factors of emotionality (empathy), self-control (stress management, low impulsivity), and self-motivation. Emotionality, involving empathy, has been widely cited in the medical and veterinary education literature as one of the most important skills in building client–patient relationships.27,56–58 Research in the health professions reinforces the desirability of EI attributes by service users,13,14,30 as does the veterinary literature, which reports that clients place more importance on a veterinarian's communication skills and emotion-related abilities than on technical expertise.59 It is therefore not surprising that healthcare educators view client input into curriculum as pivotal in producing more empathetic and client-centered graduates.60 Thus, veterinary technology curriculum designers need to ensure that the emphasis on emotionality valued by clients continues to be a focus in educational interventions such as communications training and is enhanced in areas such as clinical training.
The importance of implementing EI training for veterinary technology clinical educators is backed up by the role model literature in the medical, dental, and veterinary fields, which demonstrates that clinical educators are viewed as powerful role models in socializing students through modeling desirable attributes, behaviors, and values. The literature provides recommendations for developing the non-cognitive attributes as well as the teaching skills of educators.61(p.464),62,63 According to Barnett, higher education curricula need to prepare students for a complex world requiring resilience and an ability to manage stress.53 In this study, the majority of clients agreed with the importance of veterinary technologists being able to manage stress, including the client group focused on technical skills, who placed importance on stress management and low impulsivity. This group's associated focus on clinical skills such as triage and blood collection, which often occur in high stress situations, matched well with their EI expectations. In addition, work-related stress has been commonly cited in the veterinary literature as an issue for practicing veterinarians, with recommendations for teaching of stress management skills in undergraduate veterinary education.64–66 These findings all provide justification for the inclusion of stress management training in veterinary technology curriculum. Strategies could include mindfulness-based stress reduction training, found to be efficacious in a systematic review of stress management programs for medical students.67 Mentoring, which has been used as an evidence-based intervention for developing stress management skills in new nursing graduates,68 could similarly be emphasized in the role of clinical educators of veterinary technology students.
In addition to using role modeling and stress management training, veterinary technology curriculum designers could take note of the significant associations found between client demographics and veterinary technology attributes and skills in this study. These findings emphasized the diversity of veterinary clients based on demographics such as age, gender, and their loyalty to the veterinary practice (number of visits annually and number of years as a client). These differences have implications for interacting with clients in the clinical environment. Compared to male clients, female clients would be more likely to respond to a veterinary technologist who was sociable and empathetic. In addition, mature-aged clients (over 49 years) would be more likely to appreciate veterinary technologists being flexible, cheerful, and agreeable. This view of older clients is reflected in the inclusion of flexibility and adaptability in the list of Australian government employability skills, a set of non-technical skills and knowledge considered essential for participation in the workforce.69 This also aligns with Barnett's calling for higher education curricula that develop qualities such as resilience to prepare students for a complex, changing world.53,70 Similarly, the UQ graduate attributes, a higher education response to employability skills, state that graduates should be able to “adapt innovatively to changing environments.”71 It could therefore be suggested that, as well as employer input, client input into graduate attributes would be advantageous in addressing this curricular challenge.
Another important finding about the diversity of clients was revealed in the cluster analysis, an approach not previously attempted in veterinary technology research. Similar use of cluster analysis has been reported in other professions such as in human nursing to identify the perception of needs of family members visiting intensive care units,72 and in business to identify client groups for marketing purposes.45 Thus, this type of analysis could be useful in future studies to inform veterinary technology curricula and to determine client service needs. Overall, the differences in clients identified in this study highlighted the need for a holistic approach to veterinary technology curricula that emphasizes the three domains of professional competence, thereby preparing graduates to meet the needs of diverse groups of clients in a complex and changing world.53,54(p.132)
Client service needs were further illuminated by the interview results (n=3). Clients clearly articulated how the EI of veterinary technology graduates positively influenced the quality of their practice visit, commencing with an expectation of the veterinary technologist being empathetic, acting calmly, and helping “to settle everyone down.” Interviews also revealed that clients wanted to interact with veterinary technologists who could communicate effectively, providing “clear information” and a constant flow of information. Knowledgeability was also valued, as veterinary technologists were seen as “the only other person who can tell you what you need to know.” It was also evident that clients wanted to engage with support personnel who were knowledgeable, technically competent (“knows what they are doing”), and had a commitment to ongoing professional development (“keen to learn and progress their knowledge base”)—essentially a professionally competent graduate. These findings are congruent with Dall'Alba's premise in medical education of a need to focus on the three elements of professional competence: knowing, doing, and being.54(p.34) This educational model is consistent with twenty-first century medical health care, which is viewed as a collaboration between patients and medical practitioners.54(p.132) A similar message is evident in the veterinary client interviews, and provides compelling evidence for client input to shape a market-driven veterinary technology curriculum.
In addition to EI and technical skills, professional attributes essential for teamwork and reflection were explored in this study. Even though these findings were very relevant to the design of a professionalizing veterinary technology curriculum, they are beyond the scope of this paper.
Valuable data for innovative veterinary technology curriculum design were collected from the study. There were, however, some limitations with regard to the veterinary practice and client samples, data collection, and participant response rate. The limitations of the client sample being drawn from a limited number of veterinary practices were counteracted by participants being randomly selected from a large client population, and the practices being key employers of veterinary technology graduates and clinical placement providers for veterinary technology students. On this basis, the clients who participated may be considered to be representative of veterinary clients who interact with veterinary technology graduates. The direction of the gender bias on client responses to study items is difficult to predict, as the population visiting veterinary practices may not reflect the general population. For example, one veterinary practice client population in the study was 70% female.
If selection bias existed in this study, then the direction of the bias would be away from the null and therefore the effect of gender may have been overestimated and vice versa. The overall response rate (24%) was low and this may have induced a non-response bias affecting the external validity of the results. As no data were collected from non-respondents, the extent of the bias could not be determined and warrants further investigations. Furthermore, a larger client sample size would have provided greater understanding of how clients' perceptions of the importance of attributes and skills might vary with client demographics. Consequently, caution is required in interpretation of results and the generalizability of these findings. This flaw in the external validity was compensated by rigor in the internal validity of the research, including the use of interviews for triangulation of data and methods and the randomization of the client sample. In addition, the majority of clients recognized the negatively worded EI items in the questionnaire, indicating a meaningful response, and the statistically significant differences in some results also reflected internal validity.
This study was driven by a gap in the literature regarding the role of veterinary clients as key stakeholders in curriculum design for veterinary technology, an emerging profession in Australasia. Overall, client responses indicated a desire for a professionally competent veterinary technology graduate, suggesting the need for a curriculum focused on the three domains of learning: the affective, cognitive, and technical domains. Another important finding was that groups of clients differed in the importance they placed on each of these domains, with some clients being highly focused on EI and others more focused on the technical expertise of veterinary technologists. Significant differences were also found associated with client gender and age, and with those who visited the veterinary practice more than twice per year or stayed with a practice for several years. These findings again highlighted the diversity of veterinary clients and that, in meeting their service needs, “one size does not fit all.” Curriculum designers therefore need to recognize this and seek diverse client input into curricula. Additional recommendations for a market-driven veterinary technology curriculum include enhanced EI training for students, communications training, and mindfulness techniques for stress management. Another recommendation is to provide EI and stress management training for clinical educators, who shape the attitudes and behaviors of veterinary technology undergraduates, to meet the needs of diverse client groups in complex clinical environments. Furthermore, the results from this study could be valuable in the recruitment and training of veterinary support personnel for client-centered service provision to enhance the sustainability of veterinary practices.
ACKNOWLEDGMENTS
The authors would like to thank the veterinary practice principals, and the staff and clients who volunteered their support for this study. Gratitude is also expressed for the contributions of Dr. Katsumi Oshioka, NVLU, Japan, and Dr. Worawut Rerkamnuaychoke, Kasetsart University, Thailand.
a Microsoft© Excel 2007, Microsoft© Corporation.
b R Development Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
| 1. | Hunt A, Adamson B, Higgs J, et al. University education and the physiotherapy professional. Physiotherapy. 1998;84(6):264–73. http://dx.doi.org/10.1016/S0031-9406(05)65527-7 Google Scholar |
| 2. | Etzioni A. The semi-professions and their organisations: teachers, nurses, social workers. New York, NY: Free Press; 1969 Google Scholar |
| 3. | Moodie G, Fredman N, Bexley E, et al. Vocational education's variable links to vocations. Adelaide: National Centre for Vocational Education Research; 2013 Google Scholar |
| 4. | Clarke PM, Schull DN, Coleman GT. Demographics and employment destinations of a new group of veterinary technologists in Australia. J Vet Med Educ. 2009;36(2):241–5. http://dx.doi.org/10.3138/jvme.36.2.241. Medline:19625675 Link, Google Scholar |
| 5. | Massey University. Programmes and papers: Bachelor of Veterinary Technology (BVetTech) [Internet]. Palmerston North, New Zealand: Massey University; 2015 [cited 2014 Dec 12]. Available from: http://www.massey.ac.nz/massey/learning/programme-course-paper/programme.cfm?prog_id=93432 Google Scholar |
| 6. | Rerkamnuaychoke W (Kasetsart University, Bangkok, Thailand). E-mail to: Patricia M. Clarke (School of Veterinary Science, University of Queensland, Australia). 2014 Mar 17 Google Scholar |
| 7. | Ishioka K (Department of Veterinary Nursing, School of Veterinary Nursing & Technology, Faculty of Veterinary Science, Nippon Veterinary and Life Science University, Tokyo, Japan). E-mail to: Patricia M. Clarke (School of Veterinary Science, University of Queensland, Australia). 2014 Dec 29 Google Scholar |
| 8. | American Veterinary Medical Association. Model Veterinary Practice Act —January 2013 [Internet]. Schaumburg, IL: American Veterinary Medical Association; 2013 [cited 2015 Mar 20]. Available from: https://www.avma.org/KB/Policies/Pages/Model-Veterinary-Practice-Act.aspx Google Scholar |
| 9. | Veterinary Nurses Council of Australia. Frequently asked questions [Internet]. Victoria, Australia: Veterinary Nurses Council of Australia; 2012 [cited 2015 Mar 18]. Available from: http://www.vnca.asn.au/about-vnca/frequently-asked-questions/ Google Scholar |
| 10. | Royal College of Veterinary Surgeons. I want to be a veterinary nurse: training routes [Internet]. London, UK: Royal College of Veterinary Surgeons; 2015 [cited 2015 Mar 18]. Available from: http://www.rcvs.org.uk/education/i-want-to-be-a-veterinary-nurse/ Google Scholar |
| 11. | Fallon D, Warne T, McAndrew S, et al. An adult education: learning and understanding what young service users and carers really, really want in terms of their mental well being. Nurse Educ Today. 2012;32(2):128–32. http://dx.doi.org/10.1016/j.nedt.2011.06.002. Medline:21752499 Medline, Google Scholar |
| 12. | Happell B. Mental health nursing and education: walking the talk—consumer participation in CQUniversity curriculum. Aust Nurs J 2010;18(3):34. http://search.informit.com.au/documentSummary;dn=345139508229659;res=IELHEA Google Scholar |
| 13. | Hurley J. The necessity, barriers and ways forward to meet user-based needs for emotionally intelligent nurses. J Psychiatr Ment Health Nurs. 2008;15(5):379–85. http://dx.doi.org/10.1111/j.1365-2850.2007.01243.x. Medline:18454823 Medline, Google Scholar |
| 14. | Morgan A, Jones D. Perceptions of service user and carer involvement in healthcare education and impact on students' knowledge and practice: a literature review. Med Teach. 2009;31(2):82–95. http://dx.doi.org/10.1080/01421590802526946. Medline:19090412 Medline, Google Scholar |
| 15. | Rhodes CA. User involvement in health and social care education: a concept analysis. Nurse Educ Today. 2012;32(2):185–9. http://dx.doi.org/10.1016/j.nedt.2011.11.012. Medline:22153057 Medline, Google Scholar |
| 16. | Duyvendak JW, Knijn T, Kremer M, editors. Policy, people and the new professional: de-professionalisation and re-professionalisation in care and welfare. Amsterdam: Amsterdam University Press; 2006. http://dx.doi.org/10.5117/9789053568859 Google Scholar |
| 17. | Brown JP, Silverman JD. The current and future market for veterinarians and veterinary medical services in the United States. J Am Vet Med Assoc. 1999;215(2):161–83. Medline:10416465 Medline, Google Scholar |
| 18. | Nielsen NO. Will the veterinary profession flourish in the future? J Vet Med Educ. 2003;30(4):301–7. http://dx.doi.org/10.3138/jvme.30.4.301. Medline:14976614 Link, Google Scholar |
| 19. | Rubin HE, and the National Commission on Veterinary Economic Issues. Reversing the downward trend. J Am Vet Med Assoc. 2003;223(3):306–7. http://dx.doi.org/10.2460/javma.2003.223.306. Medline:12906223 Medline, Google Scholar |
| 20. | National Research Council of the National Academies. Workforce needs in veterinary medicine [Internet]. Washington, DC: National Academies Press; 2011 [cited 2014 Dec 12]. Available from: http://www.nap.edu/download.php?record_id=13413 Google Scholar |
| 21. | Bland CJ, Starnaman S, Wersal L, et al. Curricular change in medical schools: how to succeed. Acad Med. 2000;75(6):575–94. http://dx.doi.org/10.1097/00001888-200006000-00006. Medline:10875502 Medline, Google Scholar |
| 22. | Gamble N, Patrick C, Peach D. Internationalizing work-integrated learning: creating global citizens to meet the economic crisis and skills shortage. HERD. 2010;29(5):535–46 Google Scholar |
| 23. | Walkington J. A process for curriculum change in engineering education. Eur J Eng Educ. 2002;27(2):133–48. http://dx.doi.org/10.1080/03043790210129603 Google Scholar |
| 24. | Jevring C. Do your support staff lay golden eggs? In Pract. 1993;15(4):198–201. http://dx.doi.org/10.1136/inpract.15.4.198 Google Scholar |
| 25. | Hancock G, Schubert C. The utilisation of veterinary nurses in practice. EJCAP. 2007;17(2):191–5 Google Scholar |
| 26. | Landsberg GM, Shaw J, Donaldson J. Handling behavior problems in the practice setting. Vet Clin North Am Small Anim Pract. 2008;38(5):951–69. http://dx.doi.org/10.1016/j.cvsm.2008.04.002. Medline:18672148 Medline, Google Scholar |
| 27. | Shaw JR. Four core communication skills of highly effective practitioners. Vet Clin North Am Small Anim Pract. 2006;36(2):385–96. http://dx.doi.org/10.1016/j.cvsm.2005.10.009. Medline:16442449 Medline, Google Scholar |
| 28. | Mossop L, Gray C. Teaching communication skills. In Pract. 2008;30(6):340–3. http://dx.doi.org/10.1136/inpract.30.6.340 Google Scholar |
| 29. | Bloom BS, Engelhart MD, Furst EJ, et al, editors. Taxonomy of educational objectives: the classification of educational goals. Vol. 1, Cognitive domain. London: Longman, Green & Co.; 1956 Google Scholar |
| 30. | Freshwater D, Stickley T. The heart of the art: emotional intelligence in nurse education. Nurs Inq. 2004;11(2):91–8. http://dx.doi.org/10.1111/j.1440-1800.2004.00198.x. Medline:15154888 Medline, Google Scholar |
| 31. | Petrides KV, Pita R, Kokkinaki F. The location of trait emotional intelligence in personality factor space. Br J Psychol. 2007;98(2):273–89. http://dx.doi.org/10.1348/000712606X120618. Medline:17456273 Medline, Google Scholar |
| 32. | Cherry MG, Fletcher I, O'Sullivan H, et al. Emotional intelligence in medical education: a critical review. Med Educ. 2014;48(5):468–78. http://dx.doi.org/10.1111/medu.12406. Medline:24712932 Medline, Google Scholar |
| 33. | Mayer JD, Salovey P, Caruso DR, et al. Emotional intelligence as a standard intelligence. Emotion. 2001;1(3):232–42. http://dx.doi.org/10.1037/1528-3542.1.3.232. Medline:12934682 Medline, Google Scholar |
| 34. | Bar-On R. The emotional intelligence inventory (EQ-i): technical manual. Toronto: Multi-Health Systems; 1997 Google Scholar |
| 35. | Goleman G. Working with emotional intelligence. New York: Bantam Books; 1995 Google Scholar |
| 36. | Petrides KV, Furnham A, Frederickson N. Emotional intelligence. Psychologist. 2004;17(10):574–7. https://thepsychologist.bps.org.uk/volume-17/edition-10/emotional-intelligence Google Scholar |
| 37. | Cresswell JW. Research design: qualitative, quantitative and mixed methods approaches. 4th ed. Thousand Oaks, CA: SAGE Publications; 2014. Google Scholar |
| 38. | Australian Government ComLaw. National Health and Medical Research Council Act 1992 [Internet]. Commonwealth of Australia; 2014 [cited 2014 Dec 20]. Available from: http://www.comlaw.gov.au/Series/C2004A04516 Google Scholar |
| 39. | Lodico MG, Spaulding DT, Voegtle KH. Methods in educational research: from theory to practice. 2nd ed. San Francisco, CA: Jossey-Bass, 2010 Google Scholar |
| 40. | Petrides KV, Furnham A. Trait emotional intelligence: psychometric investigation with reference to established trait taxonomies. Eur J Pers. 2001;15(6):425–48. http://dx.doi.org/10.1002/per.416 Google Scholar |
| 41. | de Laforcade AM, Morrissey PA, Rowell SL, et al. Market survey investigating the feasibility of an advanced training program for veterinary technicians. J Am Vet Med Assoc. 2005;226(3):366–9. http://dx.doi.org/10.2460/javma.2005.226.366. Medline:15702684 Medline, Google Scholar |
| 42. | Grimes DA, Schulz KF. Making sense of odds and odds ratios. Obstet Gynecol. 2008;111(2, Part 1):423–6. http://dx.doi.org/10.1097/01.AOG.0000297304.32187.5d. Medline:18238982 Medline, Google Scholar |
| 43. | Enticott JC, Kandane-Rathnayake RK, Phillips LE. Odds ratio simplified. Transfusion. 2012;52(3):467–9. http://dx.doi.org/10.1111/j.1537-2995.2011.03429.x. Medline:22070765 Medline, Google Scholar |
| 44. | Faraway JJ. Extending the linear model with R: generalized linear, mixed effects, and nonparametric regression models. Boca Raton, FL: Chapman & Hall/CRC; 2006 Google Scholar |
| 45. | Burns RB, Burns RA. Cluster analysis [Internet]. London, UK: Sage Publications [cited 2015 Mar 18]. Available from: http://www.uk.sagepub.com/burns/website%20material/Chapter%2023%20-%20Cluster%20Analysis.pdf Google Scholar |
| 46. | Doron J, Trouillet R, Maneveau A, et al. Coping profiles, perceived stress and health-related behaviors: a cluster analysis approach. Health Promot Int. 2015;30(1):88–100. http://dx.doi.org/10.1093/heapro/dau090. Medline:25324530 Medline, Google Scholar |
| 47. | Everitt BS, Landau S, Leese M. Cluster analysis. New York, NY: Oxford University Press; 2001 Google Scholar |
| 48. | Ward JH Jr. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58(301):236–44. http://dx.doi.org/10.1080/01621459.1963.10500845 Google Scholar |
| 49. | Gibbs G. Analyzing qualitative data. Los Angeles, CA: Sage Publications, 2007 Google Scholar |
| 50. | Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101. http://dx.doi.org/10.1191/1478088706qp063oa Google Scholar |
| 51. | Forsyth PB, Danisiewcz TJ. Towards a theory of professionalization. Work Occup. 1985;12(1):59–76. http://dx.doi.org/10.1177/0730888485012001004 Google Scholar |
| 52. | Larson MS. The rise of professionalism: a sociological analysis. Berkeley, CA: University of California Press; 1977 Google Scholar |
| 53. | Barnett R. Learning for an unknown future. HERD. 2012;31(1):65–77 Google Scholar |
| 54. | Dall'Alba G. Learning to be professionals: innovation and change in professional education. Dordrecht, Netherlands: Springer; 2009. http://dx.doi.org/10.1007/978-90-481-2608-8 Google Scholar |
| 55. | Mellanby RJ, Rhind SM, Bell C, et al. Perceptions of clients and veterinarians on what attributes constitute ‘a good vet’. Vet Rec. 2011;168(23):616. http://dx.doi.org/10.1136/vr.d925 Medline, Google Scholar |
| 56. | Bonvicini KA. Empathy: what's all the fuss about? International Conference on Communication in Veterinary Medicine; 2008; Banff, Canada Google Scholar |
| 57. | Batt-Rawden SA, Chisolm MS, Anton B, et al. Teaching empathy to medical students: an updated, systematic review. Acad Med. 2013;88(8):1171–7. http://dx.doi.org/10.1097/ACM.0b013e318299f3e3. Medline:23807099 Medline, Google Scholar |
| 58. | McArthur ML, Fitzgerald JR. Companion animal veterinarians' use of clinical communication skills. Aust Vet J. 2013;91(9):374–80. http://dx.doi.org/10.1111/avj.12083. Medline:23980830 Medline, Google Scholar |
| 59. | Tinga CE, Adams CL, Bonnett BN, et al. Survey of veterinary technical and professional skills in students and recent graduates of a veterinary college. J Am Vet Med Assoc. 2001;219(7):924–31. http://dx.doi.org/10.2460/javma.2001.219.924. Medline:11601787 Medline, Google Scholar |
| 60. | Illingworth P, Chelvanayagam S. Benefits of interprofessional education in health care. Br J Nurs. 2007;16(2):121–4. http://dx.doi.org/10.12968/bjon.2007.16.2.22773. Medline:17353824 Medline, Google Scholar |
| 61. | Sutkin G, Wagner E, Harris I, et al. What makes a good clinical teacher in medicine? A review of the literature. Acad Med. 2008;83(5):452–66. http://dx.doi.org/10.1097/ACM.0b013e31816bee61. Medline:18448899 Medline, Google Scholar |
| 62. | Brown J, Stevens J, Kermode S. Supporting student nurse professionalisation: the role of the clinical teacher. Nurse Educ Today. 2012;32(5):606–10. http://dx.doi.org/10.1016/j.nedt.2011.08.007. Medline:21907468 Medline, Google Scholar |
| 63. | Schull DN, Kyle GJ, Coleman GT, et al. Attributes of clinical role models as described by senior veterinary students in Australia. J Vet Med Educ. 2012;39(3):263–6. http://dx.doi.org/10.3138/jvme.0311-032R. Medline:22951461 Link, Google Scholar |
| 64. | Hatch PH, Winefield HR, Christie BA, et al. Workplace stress, mental health, and burnout of veterinarians in Australia. Aust Vet J. 2011;89(11):460–8. http://dx.doi.org/10.1111/j.1751-0813.2011.00833.x. Medline:22008127 Medline, Google Scholar |
| 65. | Epp T, Waldner C. Occupational health hazards in veterinary medicine: physical, psychological, and chemical hazards. Can Vet J. 2012;53(2):151–7. Medline:22851776 Medline, Google Scholar |
| 66. | Skipper GE, Williams JB. Failure to acknowledge high suicide risks among veterinarians. J Vet Med Educ. 39(1):79–82, 2012. http://dx.doi.org/10.3138/jvme.0311.034R Link, Google Scholar |
| 67. | Shiralkar MT, Harris TB, Eddins-Folensbee FF, et al. A systematic review of stress-management programs for medical students. Acad Psychiatry. 2013;37(3):158–64. http://dx.doi.org/10.1176/appi.ap.12010003. Medline:23446664 Medline, Google Scholar |
| 68. | Theisen JL, Sandau KE. Competency of new graduate nurses: a review of their weaknesses and strategies for success. J Contin Educ Nurs. 2013;44(9):406–14. http://dx.doi.org/10.3928/00220124-20130617-38. Medline:23799789 Medline, Google Scholar |
| 69. | Ithaca Group. Employability skills framework stage 1: final report [Internet]. Department of Education, Employment and Workplace Relations (Australia); 2012 [cited 2014 Jul 24]. Available from: http://www.vetpd.qld.gov.au/resources/pdf/tla/employability-skills-framework-phase-1-report.pdf Google Scholar |
| 70. | Barnett R. Knowing and becoming in the higher education curriculum. Stud High Educ. 2009;34(4):429–40. http://dx.doi.org/10.1080/03075070902771978 Google Scholar |
| 71. | The University of Queensland. UQ policies and procedures library: 3.10.05 graduate attributes (5.3 independence and creativity) [Internet]. Queensland, Australia: The University of Queensland; 2014 [cited 2014 Dec 20]. Available from: http://ppl.app.uq.edu.au/content/3.10.05-graduate-attributes#Procedures. Google Scholar |
| 72. | Hinkle JL, Fitzpatrick E, Oskrochi GR. Identifying the perception of needs of family members visiting and nurses working in the intensive care unit. J Neurosci Nurs. 2009;41(2):85–91. http://dx.doi.org/10.1097/JNN.0b013e31819c2db4. Medline:19361124 Medline, Google Scholar |
