“Becoming a Person Who Does Self-Care”: How Health Care Trainees Naturalistically Develop Successful Self-Care Practices
Bibliographic record
Abstract
OBJECTIVES: Self-care is an ethical imperative for health professionals as it can mitigate the adverse effects of stress on professional functioning and health. Yet, there tends to be a lack of self-care among healthcare trainees and an insufficient focus on self-care in medical education. The objective of this study was to develop a grounded theory of how health trainees become successful self-care users. METHODS: Semi-structured interviews were conducted with 17 students in a variety of healthcare disciplines. Data were analyzed using grounded theory methodology. RESULTS: Health trainees underwent 4 iterative phases to become successful at self-care: Having a Wake-Up Call, Building Skills, Gaining Confidence, and Building an Identity. Our model also explained why some trainees were unsuccessful at developing self-care practices. CONCLUSION: We offer the first theory to explain how health trainees develop effective self-care habits. Understanding how self-care practices naturalistically develop has critical implications for developing interventions and curricula: By basing curricula about self-care on knowledge of what works, we have an opportunity to be more successful as educators. Indeed, other researchers have noted a lack of success in self-care and anti-burnout interventions for healthcare professionals. We conclude by discussing implications and recommendations for medical training and curriculum for health professions, including augmenting naturally occurring processes, linking self-care to personalized values, providing opportunities for deliberate practice, focusing on persistence with self-care, and faculty promotion and acceptance of trainee self-care.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".