Application of performance psychology to emergency medicine resident physicians
Bibliographic record
Abstract
Medical residents are consistently faced with high expectations and enormous performance pressures during their training. Current literature suggests this can reduce overall health and well being and impede job performance. Utilizing strategies common to the field of performance psychology, a High Performance Physician (HPP) program was designed to meet the demands of post-graduate medical residency and integrated into a residency program at a Canadian medical school's department of Emergency Medicine. The primary interest was to capture the phenomena of 22 emergency residents as they progressed through the HPP program. A qualitative approach was used which included pre and post surveys to assess the residents' perspectives and to identify areas of concern that they wanted to address. Secondly, four class sessions were held with the residents to present and discuss various topics consistent with performance psychology. Lastly, an online discussion group was used to keep ideas and discussions going in between sessions. Testimony from these online exchanges, discussions and post-surveys were printed and analyzed through a process of thematic analysis and developed into a narrative. Pre-surveys identified three areas of concern: a) maintaining perspective, b) coping effectively and c) sustaining optimal performance. Furthermore, the notion of sanctuary, both in the group sessions and online exchanges was highlighted. Recommendations for further research will be discussed, as will guidelines for qualitative research within the post-graduate medical education context.Acknowledgments: Dr. Chau Pham; Dr. Shelly Zubert; Dr. Miteb Algithami; Dr. Cal Botterill
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".