Comparing Tolerance of Ambiguity in Veterinary and Medical Students
Bibliographic record
Abstract
Current guidelines suggest that educators in both medical and veterinary professions should do more to ensure that students can tolerate ambiguity. Designing curricula to achieve this requires the ability to measure and understand differences in ambiguity tolerance among and within professional groups. Although scales have been developed to measure tolerance of ambiguity in both medical and veterinary professions, no comparative studies have been reported. We compared the tolerance of ambiguity of medical and veterinary students, hypothesizing that veterinary students would have higher tolerance of ambiguity, given the greater patient diversity and less well-established evidence base underpinning practice. We conducted a secondary analysis of questionnaire data from first- to fourth-year medical and veterinary students. Tolerance of ambiguity scores were calculated and compared using the TAMSAD scale (29 items validated for the medical student population), the TAVS scale (27 items validated for the veterinary student population), and a scale comprising the 22 items common to both scales. Using the TAMSAD and TAVS scales, medical students had a significantly higher mean tolerance of ambiguity score than veterinary students (56.1 vs. 54.1, p<.001 and 60.4 vs. 58.5, p=.002, respectively) but no difference was seen when only the 22 shared items were compared (56.1 vs. 57.2, p=.513). The results do not support our hypothesis and highlight that different findings can result when different tools are used. Medical students may have slightly higher tolerance of ambiguity than veterinary students, although this depends on the scale used.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".