56 Exploring the Wellness Needs of Paediatric Residents: An Appreciative Inquiry Approach
Bibliographic record
Abstract
It has been established that residents face high rates of burnout and depression. This issue was highlighted in the recent Canadian Medical Association National Physician Health Survey, with resident rates of burnout as much as 38% and positive depression screen in 48% of resident respondents (Canadian Medical Association 2018). Evidence is limited regarding the impact of interventions (West et al. 2016). Many current interventions focus on developing resilience on an individual basis and are not always generalizable to the specific context a medical trainee is working in. The objective of this study was to assess the wellness needs of local Paediatric Medicine trainees. A needs assessment of current trainees was conducted utilizing an appreciative inquiry approach, a qualitative method of organizational development to understand transformational change from a positive light (Hennessy, 2014). Qualitative responses were collected through a REDCap survey, which was distributed electronically. Responses were coded by three members of the project team and overarching themes were identified. Survey questions asked trainees to reflect on a difficult time where they emerged resilient, what exists in the learning environment that promotes resilience, what are the characteristics of an ideal learning environment and what changes need to be done to achieve such an environment. Survey respondents were also asked to select what formal wellness initiatives they would find helpful. 32 of 86 residents (37%) completed the survey. Paediatric medicine trainees reported feeling supported by individual relationships, but sought additional opportunities to share experiences and grow from structured activities Participants reported wanting a shift in the structure of the learning environment to create scaffolding for optimal resident learning. Participants also shared that they would like an opportunity to meet their basic needs both at work and in their lives, including physical health, mental health, and psychological safety. Finally, participants envisioned a positive shift in the culture of medicine that would help mitigate the demands created by structural barriers in the learning environment. This project highlights key areas to target in the development of wellness programming and underlines the need for structural and cultural change in medical training and learning environments to support the wellbeing of paediatric medicine trainees in their local environment.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".