Ambiguity Tolerance and Prospective Specialty Choice Among Third-Year Medical Students
Bibliographic record
Abstract
INTRODUCTION: Poor tolerance of ambiguity is consequential in clinical practice, and has been linked to avoidance of family medicine, in which there is inherently more ambiguity. This study aimed to investigate the relationship between tolerance of ambiguity and prospective specialty choice of medical students in their third year of medical school. This stage of medical training is of particular importance as students develop clinical reasoning skills and encounter clinical ambiguity. METHODS: This was a cross-sectional study using an online survey. Sixty-one third-year medical students (62% response rate) from a large Canadian university completed the survey with a validated measure of ambiguity tolerance (the 29-item Tolerance of Ambiguity in Medical Students and Doctors scale) and their top three specialty choices. Specialty choices were subsequently grouped into two categories: family medicine (FM) and non-family medicine (non-FM) specialties. RESULTS: There was no significant mean difference in tolerance of ambiguity between students who reported interest in FM and students interested in non-FM specialties. Similarly, we observed no significant difference in tolerance of ambiguity between female and male students. Older students reported higher levels of ambiguity tolerance. Older students were also more likely to report FM as one of their top three specialty choices. CONCLUSION: Qualitative studies are needed to explore possible reasons for the observed results, including the effects of digital information resources and clinical decision-making tools on medical students' ambiguity tolerance. Medical educators should be aware that some students may require explicit training in how to respond to ambiguity.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".