Group-based storytelling in disease self-management among people with diabetes
Bibliographic record
Abstract
OBJECTIVE: We explored the underlying mechanisms by which storytelling can promote disease self-management among people with type 2 diabetes. METHODS: Two, eight-session storytelling interventions were delivered to a total of eight adults with type 2 diabetes at a community health center in Toronto, Ontario. Each week, participants shared stories about diabetes self-management topics of their choice. Using a qualitative descriptive approach, transcripts from each session and focus groups conducted during and following the intervention were coded and analyzed using NVivo software. Through content analysis, we identified categories that describe processes and benefits of the intervention that may contribute to and support diabetes self-management. RESULTS: Our analysis suggests that storytelling facilitates knowledge exchange, collaborative learning, reflection, and making meaning of one's disease. These processes, in turn, could potentially build a sense of community that facilitates peer support, empowerment, and active engagement in disease self-management. CONCLUSION: Venues that offer patients opportunities to speak of their illness management experiences are currently limited in our healthcare systems. In conjunction with traditional diabetes self-management education, storytelling can support several core aspects of diabetes self-management. Our findings could guide the design and/or evaluation of future story-based interventions.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".