Mind-set matters: Negative thoughts degrade motor skill acquisition in novice medical trainees
Bibliographic record
Abstract
Peer comparison can influence a learner’s self-efficacy beliefs and skill acquisition. Our previous work has shown that novice medical trainees who believe that they are performing worse than the group average on a baseline suturing task (regardless of how they actually perform that technique), experience significant self-efficacy and performance degradation when learning a new suturing technique. Our objective was to further examine how this type of feedback influenced their strategies during independent practice time. Regardless of their actual performance on a baseline suturing task, novice trainees (n=30) were divided into one of three groups where they received either no feedback, or fabricated performance summaries indicating that they were performing better or worse than their peers. After receiving this manipulation, trainees performed and practiced a new suturing technique. At baseline, there were no differences in self-efficacy and performance. Those receiving the negative comparative feedback reported significantly lower self-efficacy and performed worse on the new suturing task compared to the other groups. Despite the degradation in psychological and behavioural outcomes, this group did not differ (p=.720) in how they practiced independently (time and number of sutures completed). These results will also be discussed in terms of the expert assessment of the video data (GRS and checklist). Our findings suggest that negative peer comparison is detrimental to individual performance and psychological beliefs notwithstanding having had the same amount of physical practice as the other groups.
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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 | 0.001 | 0.017 |
| 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 | 0.001 | 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".