Narrative Care and Engagement in Social and Health Care: Enhancing Identity with a Small Story Approach
Bibliographic record
Abstract
Narrative care, an approach developed from the larger concept of narrative gerontology, considers the importance of stories as a source of identity. A type of person-centered care, narrative care in care settings encourages care workers to elicit stories to gain a more wholistic understanding of the person. Drawing on personal experience in the field, I argue that although “big” story approaches (e.g., grand life narratives) have typically been used in social and healthcare settings, “small” story approaches (e.g., snippets or moments) are more practical for care workers. The expansion of the concept of narrative care to include “narrative engagement” will be explored, which if applied in meaningful ways can promote citizenship, shift power dynamics, generate empowerment, and create systemic change in social and health care settings. Finally, newly developed train-the-trainer narrative care training will be discussed, which is designed to meet the needs of diverse social/health care workers, with a focus on meaningful methods of adopting narrative care and engagement in practice.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".