A year in transition: a qualitative study examining the trajectory of first year residents’ well-being
Bibliographic record
Abstract
BACKGROUND: It is generally understood that trainees experience periods of heightened stress during first year residency, yet there is little information on variations in stress and well-being over the transition period or those factors that contribute to these variations. This qualitative study explored the trajectory of well-being described by first year residents in the context of challenges, supports and adaptations over time. METHODS: In-depth interviews were conducted face-to-face with 17 first year residents at the University of Toronto. Participants drew a graph of their well-being over the course of their first year and described critical periods of challenge and adaptation. Interviews were audio-taped and transcribed. Results were organized into a thematic analysis using NVivo software. RESULTS: Residents described a pattern of well-being that varied in accordance with changes in rotations. Well-being increased when residents perceived high levels of team support, felt competent and experienced valued learning opportunities. Well-being decreased with low team support, heavy work demands, few learning opportunities and poor orientations. Anxiety and excitement in the beginning of the year gave way to heightened confidence but increased fatigue and apathy towards the year's end. Residents used a number of cognitive, behavioural and self-care strategies to cope with transitional challenges. CONCLUSIONS: Residents experienced a pattern of highly fluctuating well-being that coincided with changes in rotations. Residents' well-being varied according to levels of supervisor and colleague support, learning opportunities, and work demands. Residents' well-being may be improved by program interventions that facilitate better team and supervisory supports, maintain optimal service to learning ratios, establish effective fatigue and risk management systems, offer wellness support services and integrate skills based resiliency training into the curriculum.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".