Can Writing and Storytelling Foster Self-care?
Bibliographic record
Abstract
Research into self-care practices suggests the need for conscientious and systematic support of nurses and other health care providers. The purpose of this study was to explore the impact of an innovative self-care initiative. The goals were to explore the experience of nurses and other health care providers participating in a reflective, creative nonfiction storytelling event called "Dinner and Stories" and the potential benefits and limitations of using an informal, storytelling model for self-care. A qualitative narrative design was used. Twenty-seven participants including nurses, social workers, and hospice volunteers wrote creative nonfiction stories about a lingering experience of providing care. At predefined dates, groups of up to six met for dinner in a home setting. Participants read aloud, listened deeply, and discussed their narrated stories. Four sources of data were collected: creative nonfiction stories, online forum discussions, in-depth interviews, and host facilitator field notes. Researchers identified four themes: (1) needing a self-care culture, (2) storytelling and writing as healing, (3) co-creating layers of connection, and (4) preferring face-to-face contact. Results add to knowledge about the therapeutic benefits of writing and storytelling for nurses and other health care providers including enriched meaning-making, emotional conveyance, and therapeutic connections between storytellers and listeners.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".