What should we be selecting for? A systematic approach for determining which personal characteristics to assess for during admissions
Bibliographic record
Abstract
BACKGROUND: Admission committees are responsible for creating fair, defensible, reliable, and valid processes that assess those attributes considered important for professional success. There is evidence for the continuing use of academic ability as a selection criterion for health professional schools; however, there is little evidence for the reliability and validity of measures currently in place to assess personal characteristics. The Ontario Veterinary College (OVC) initiated a review of its admissions criteria in order to implement an evidence-based method to determine which characteristics veterinary stakeholders consider important to assess for admission. METHODS: Eleven characteristics were identified by the OVC Admissions Committee and a survey was sent to all licensed veterinarians in Ontario (n=4,068), OVC students (n=450), and OVC faculty, interns and residents (n=192). A paired comparison method was used to identify the relative rank order of the characteristics, and multivariate analysis of variance with post hoc analyses was used to determine between group differences in the returned survey data. RESULTS: Surveys were returned from 1,312 participants (27.86% response rate; female 59.70%). The relative rank of the characteristics was reasonably consistent among participant groups, with ethical behaviour, sound judgment, communication, and critical and creative thinking being ranked as the top four. However, the importance of certain characteristics like communication and empathy were perceived differently by groups. For instance, females scored communication (F(1, 1289) = 20.24, p < .001, d = .26) and empathy (F(1, 1289) = 55.41, p < .001, d = 0.42) significantly higher than males, while males scored knowledge of profession (F(1, 1289) = 12.81, p < .001, d = 0.20), leadership (F(1, 1289) = 10.28, p = .001, d = 0.18), and sound judgment (F(1, 1289) = 13.56, p < .001, d = 0.21) significantly higher than females. CONCLUSIONS: The data from the paired comparison method provide convergent evidence for the characteristics participant groups identify as most important in determining who should be admitted to a veterinary program. The between group analyses provides important information regarding characteristics most important to various subgroups; this has implications for what characteristics are selected for at admission as well as on who is selecting for them.
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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 | 0.256 | 0.393 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".