Promoting Success: A Professional Development Coaching Program for Interns in Medicine
Bibliographic record
Abstract
BACKGROUND: Residency is an intense period. Challenges, including burnout, arise as new physicians develop their professional identities. Residency programs provide remediation, but emotional support for interns is often limited. Professional development coaching of interns, regardless of their performance, has not been reported. OBJECTIVE: Design, implement, and evaluate a program to support intern professional development through positive psychology coaching. METHODS: We implemented a professional development coaching program in a large residency program. The program included curriculum development, coach-intern interactions, and evaluative metrics. A total of 72 internal medicine interns and 26 internal medicine faculty participated in the first year. Interns and coaches were expected to meet quarterly; expected time commitments per year were 9 hours (per individual coached) for coaches, 5 1/2 hours for each individual coachee, and 70 hours for the director of the coaching program. Coaches and interns were asked to complete 2 surveys in the first year and to participate in qualitative interviews. RESULTS: Eighty-two percent of interns met with their coaches 3 or more times. Coaches and their interns assessed the program in multiple dimensions (participation, program and professional activities, burnout, coping, and coach-intern communication). Most of the interns (94%) rated the coaching program as good or excellent, and 96% would recommend this program to other residency programs. The experience of burnout was lower in this cohort compared with a prior cohort. CONCLUSIONS: There is early evidence that a coaching program of interactions with faculty trained in positive psychology may advance intern development and partially address burnout.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".