The meeting of two disciplines — veterinary medicine and behavior science
Bibliographic record
Abstract
Recently, I had the opportunity to attend a workshop hosted by the National Commission on Veterinary Economic Issues (NCVEI), where the findings of Personnel Decisions International (PDI) on “Core Competencies in the Veterinary Profession” were presented for the first time. To help improve the economic health of the veterinary profession, PDI was hired by a consortium of 9 veterinary colleges in the United States to identify the competencies that contribute to success in the veterinary profession. Behavioral psychologists with PDI have extensive experience in identifying competencies associated with professional success in the business world, as well as with the development of quantitative methods to identify individuals who possess these competencies. Personnel Decisions International has developed a model called the building blocks of performance that it believes is central to successful recruitment efforts and human resource development in the business world (1). In this model, the foundation building blocks that contribute to performance are inherent traits, abilities, interests, values, and motivations that are difficult to develop in adults (in other words very hard for them to be developed by the vet colleges) and, therefore, must be selected for during the admissions process. The middle layer of blocks in the PDI model represent knowledge and experiences that are readily developed by the colleges. Blocks in the top layer of the model represent skills that are initially developed in the colleges and further honed through on-the-job training. Through the use of well established interview techniques, PDI interviewed successful veterinarians, nominated by their peers, and engaged in all aspects of the profession. Personnel Decisions International then went on to identify key behaviorally specific competencies that were common to all of the individuals who were interviewed. The competencies, which were common across all employment settings, were as follows: interpersonal (builds relationships), self management (acts autonomously, drives for results, demonstrates integrity, pursues development, demonstrates adaptability), communication (communicates effectively), leadership (motivates others, influences others, coaches and develops others), thinking (uses sound judgement, thinks innovatively), and practice/business (business oriented). Personnel Decisions International recommended that veterinary colleges develop ways to select for the “difficult to develop” competencies (uses sound judgement, thinks innovatively, acts autonomously and competently, drives for results, demonstrates integrity, pursues development, demonstrates adaptability) through the use of structured interview guides used by trained interviewers. Personnel Decisions International also suggested that veterinary colleges may want to take advantage of personality tests to identify nontechnical competencies in their applicants. Workshop participants were assured that use of structured interview guides and personality profiles are legally defensible as long as a professional job analysis is used to develop the competencies, the selection tools used have a logical link to the competencies, and the selection tools are used in a consistent manner by the colleges. As an educator at a veterinary college, I am in awe of our students. They are articulate, poised, compassionate, disciplined, resourceful, motivated, supportive of their peers, and readily assume leadership and volunteer roles. It makes me question whether an interview instrument or personality test can really improve on the students who are currently admitted to veterinary college. Yet, the profession is telling the colleges that their graduates possess excellent scientific knowledge and clinical competence but are lacking in some of the “difficult to develop” skills required for career success. The CVMA Task Force on the Future of the Veterinary Profession had in its report “Veterinary Medicine in Canada: Opportunity for Renewal” the following recommendations (2): “To ensure that individuals with good interpersonal skills and broad interests, not only individuals with exceedingly high academic qualifications, gain entry into veterinary medicine, the Task Force recommends that veterinary colleges: a) adopt a basic academic standard for admission beyond which candidates are assessed on a broad range of aptitudes as proposed in the PEW report, and b) recognize aptitude testing and personality profiling as pivotal in the selection process.” In a critique of the second recommendation it was pointed out that aptitude tests and personality profiles have efficacy only if they are used appropriately and interpreted by professionals, that they are intended to be used diagnostically and not competitively, and that most veterinary students can outsmart them (3). Maybe the time has come to take another look at the use of structured interview guides and personality tests as selection tools for admission to veterinary college. Highly successful businesses work closely with behavior scientists to identify success competencies and to quantify them in the interview process, so is it not possible that a similar approach would work in the veterinary college admission process? I, for one, will be closely following the veterinary colleges that adopt the selection tools developed by PDI. Ultimately, the question will be whether the graduates produced by using the selection tools developed by PDI are any more successful in their careers than those admitted under current admissions practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.051 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".