Development of a New Scale to Measure Ambiguity Tolerance in Veterinary Students
Bibliographic record
Abstract
The ability to cope with ambiguity and feelings of uncertainty is an essential part of professional practice. Research with physicians has identified that intolerance of ambiguity or uncertainty is linked to stress, and some authors have hypothesized that there could be an association between intolerance of ambiguity and burnout. We describe the adaptation of the TAMSAD (Tolerance of Ambiguity in Medical Students and Doctors) scale for use with veterinary students. Exploratory factor analysis supports a uni-dimensional structure for the Ambiguity tolerance construct. Although internal reliability of the 29-item TAMSAD scale is reasonable (α=.50), an alternative 27-item scale (drawn from the original 41 items used to develop TAMSAD) shows higher internal reliability for veterinary students (α=.67). We conclude that there is good evidence to support the validity of this latter TAVS (Tolerance of Ambiguity in Veterinary Students) scale to study ambiguity tolerance in veterinary students.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".