Association Between Family Medicine Residents’ Mindsets and In-Training Exam Scores
Bibliographic record
Abstract
INTRODUCTION: In medical practice, a mastery mindset is important for engaging in lifelong learning. The objective of this study was to examine the association between family medicine residents' scores on mindset measures and their performance on in-training examinations (ITE). METHODS: This was a secondary data analysis of a cohort of family medicine residents. Following ethics approval, residents' ITE scores from each of the 2 years of residency were linked with residents' responses to a mindsets survey that they had taken at the midpoint of residency training. Multiple regression analysis was used to investigate the relationship between residents' mindset scores and their ITE scores. Of 85 residents, 46 (54%) had complete data for the three data collection points. RESULTS: =.004). CONCLUSION: While the observed negative relationship between residents' mastery mindset scores and their ITE performance may be disconcerting, it is not surprising. In clinical settings, residents are individually coached by preceptors and provided with specific, actionable feedback to support their learning. With respect to formative assessments, residents likely require explicit training on how to use their assessment results (ITE scores) to support their self-directed learning. This finding has practical implications for residency programs in using ITEs as formative assessments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".