Finding the sweet spot: Developing, implementing and evaluating a burn out and compassion fatigue intervention for third year medical trainees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical trainees are at high risk for developing burnout. Introducing trainees to the risks of burnout and supporting identification and proactive responses to their 'warning' signs of compassion fatigue (CF) is critical in building resiliency. The authors developed and evaluated a burnout and CF program for third year trainees at a Canadian Medical School. Of 165 medical trainees who participated in the burnout and CF program, 59 (36%) provided evaluation and feedback of the program and its impact throughout their year. Participation included self-utilization of a validated CF and burnout tool (ProQOL) across three time-points, workshop feedback, and focus group participation. Results highlighted the importance of 1) Recognizing Individual Signs & Symptoms of Stress, CF and Burnout; 2) Normalizing Stress, CF and Burnout for Students and Physicians; 3) Learning to Manage One's Own Stress. A decrease in compassion satisfaction and increase in burnout between beginning and end of third year were found. Further outcomes highlighted the importance of learning, living and surviving CF and burnout in clerkship. Emergent theory reveals the important responsibility educators have to integrate CF and burnout programs into 'the sweet spot' that third year offers, as trainees shift from theoretical to experiential practice as future clinicians.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it