Where does resiliency fit into the residency training experience: a framework for understanding the relationship between wellness, burnout, and resiliency during residency training
Bibliographic record
Abstract
BACKGROUND: Medical literature reports high rates of burnout among medical professionals. The terms wellness and resilience are often used interchangeably in the solution focused discussions. By confusing the terminology, the distinct role of resilience within residency training may be ill-appreciated.The objective of this paper is to define wellness, burnout, and resiliency and detail a proposed framework to explain how these terms manifest within residency training. METHODS: MEDLINE and EMBASE searches were performed using the key words "resilience," "residency," "wellness," and "burnout." The search was limited to English language articles published between 2003-2017. RESULTS: The authors propose a framework based on the literature review. This work supports the implementation of resilience-based interventions that enable residents to engage with workplace adversity in a healthy way, acquire skills during the process and avoid resource depletion and, ultimately, burnout. CONCLUSION: This framework can be used by residency programs to promote educators and residents' understanding of the unique role of resilience within residency, and the importance of differentiating between wellness and resilience initiatives. Future research is required to study the utility of this framework.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".