A simple strategy to reduce stereotype threat for orthopedic residents
Bibliographic record
Abstract
BACKGROUND: Stereotype threat, defined as the predicament felt by people in either positive or negative learning experiences where they could conform to negative stereotypes associated with their own group membership, can interfere with learning. The purpose of this study was to determine if a simple orientation session could reduce stereotype threat for orthopedic residents. METHODS: The intervention group received an orientation on 2 occasions focusing on their possible responses to perceived poor performance in teaching rounds and the operating room (OR). Participants completed a survey with 7 questions typical for stereotype threat evaluating responses to their experiences. The questions had 7 response options with a maximum total score of 49, where higher scores indicated greater degree of experiences typical of stereotype threat. RESULTS: Of the 84 eligible residents, 49 participated: 22 in the nonintervention and 27 in the intervention group. The overall scores were 29 and 29.4, and 26.2 and 25.8 in the nonintervention and intervention groups for their survey responses to perceived poor performance in teaching rounds (p = 0.85) and the OR (p = 0.84), respectively. Overall, responses typical of stereotype threat were greater for perceived poor performance at teaching rounds than in the OR (p = 0.001). CONCLUSION: Residents experience low self-esteem following perceived poor performance, particularly at rounds. A simple orientation designed to reduce stereotype threat was unsuccessful in reducing this threat overall. Future research will need to consider longer-term intervention as possible strategies to reduce perceived poor performance at teaching rounds and in the OR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".