The erosion of ambiguity tolerance and sustainment of perfectionism in undergraduate Medical training: A study of clerkship training effects.
Bibliographic record
Abstract
Abstract Background : Medicine is a field that is simultaneously factual and ambiguous. While studies have examined medical trainees’ tolerance of ambiguity (TOA), the extent to which TOA is affected by clinical experiences and its association with other psychological factors such as perfectionism is unknown. Methods: This was a single cohort study:174 Students in the first (pre) and last (post) 12 weeks of their 3 rd year comprising of 6 core rotations were invited to participate in an online anonymous survey. The survey included demographic information along with published and validated TOA and perfectionism scales. Tolerance of Ambiguity in Medical Students and Doctors (TAMSAD) and The Big Three perfectionism scale-short form (BTPS-SF) were used to assess TOA and perfectionism respectively. Pre-Post mean comparisons and correlations were used to detect the effect of clerkship on TOA, perfectionism and their relationship. Results: 51 students responded to pre-survey, 62 responded to post-survey. Clerkship was associated with a decrease TOA (p<0.00) with pre-TOA scores at m=59.57 and post TOA at m=43.8. There was a moderate inverse correlation between TOA and perfectionism before clerkship (r=0.32) that increased slightly after clerkship (r=0.39). Clerkship was not significantly associated with levels of perfectionism (P>0.05). Those preferring primary care specialties had significantly lower rigid and total perfectionism scores in pre clerkship than those choosing other specialties, this difference was not found post clerkship. Conclusion: Clerkship does appear to influence student’s tolerance of ambiguity. However, perfectionism remained unchanged. Further work needs to be done exploring tailoring educational interventions to extremes of TOA and perfectionism.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".