Finding the sweet spot: Developing, implementing and evaluating a burn out and compassion fatigue intervention for third year medical trainees
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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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".