When Illness and Loss Hit Close to Home—Do Health Care Providers Learn How to Cope?
Bibliographic record
Abstract
INTRODUCTION: Up to 85% of newly qualified physicians report loss or illness in themselves or a loved one. These experiences can intensify feelings of grief in the professional setting, but the range of formal training that addresses personal illness or loss is unknown. This study aimed to explore interventions that teach health care providers and trainees about personal illness experience. METHODS: A scoping review was conducted by searching three bibliographic databases using the terms "illness," "personal," "education," and synonyms. Article screening was performed in duplicate to identify studies that described an intervention that included teaching or learning on personal experiences with illness or loss for health care providers and trainees. RESULTS: The search yielded 4168 studies, of which 13 were included. Education most often targeted medical students (54%), resident or attending physicians (31%), and nurses (31%). Other participants included social workers and psychologists. Personal illness was most frequently taught for reflection in the context of palliative care curricula (54%). Only two studies' primary purpose was to teach about coping with grief related to personal experiences. No studies within the scope of our defined methodology described training on how to support colleagues or trainees facing personal illness or loss. Reported outcomes included improved coping skills, decreased stress, and better ability to support bereaving patients. DISCUSSION: Specific training on personal illness experience is limited, with gaps in continuity of learning, particularly for continuing medical education. Future curricula can equip providers with coping strategies while enabling improved resilience and patient 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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".