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 perfectionism is unknown. Methods: This was a multiple sampling, single cohort study of students in their first year of clinical clerkship which is comprised of 6 core rotations. Consenting students completed an online anonymous survey assessing their tolerance of ambiguity (TOA) and perfectionism in their first (pre) and last (post) 12 weeks of their clinical clerkship year. 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 of TOA and perfectionism were assessed via t-tests. Results: From a total possible sample of 174 clinical clerkship students, 51 students responded to pre-survey, 62 responded to post-survey. Clerkship was associated with a significant decrease in TOA (p=0.00) with mean pre-TOA scores of 59.57 and post TOA of 43.8. Perfectionism scores were not significantly different over time (p>0.05). There was a moderate inverse correlation between TOA and perfectionism before clerkship (r=0.32) that increased slightly after clerkship (r=0.39). Those preferring primary care specialties had significantly lower rigid and total perfectionism scores in pre-clerkship than those choosing other specialties, but this difference was not found post-clerkship. Conclusion: We hypothesize that the decrease in TOA seen in this study may reflect an underlying anxiety cycle related to frequent rotation changes. Implications of the role of perfectionism and primary care is discussed. Overall these results require further investigation to better characterize the role of clinical exposure on TOA.
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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 | 0.002 | 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 | 0.001 |
| 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".