Storytelling to Support Disease Self-Management by Adults With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVES: This pilot project aimed to examine the acceptability and feasibility of a group storytelling intervention to support self-management among adults living with type 2 diabetes. METHODS: Two waves of a single-arm storytelling intervention, consisting of 8 sessions at a community health centre, were delivered to 8 adults with type 2 diabetes. Diabetes educators facilitated each session, in which patients shared stories about diabetes-self-management topics of their choice. Focus groups with both patients and facilitators explored the feasibility and acceptability of the sessions. External raters assessed the fidelity of the intervention's implementation. RESULTS: Overarching themes describe the acceptability and feasibility of the intervention: 1) the facilitation of patient self-direction, group cohesion, collective learning and support; 2) roles of facilitator educator, and peer learner; 3) the intervention's customization to patients' preferences. The sessions were delivered with high fidelity (averaging 84.4%). CONCLUSIONS: Informal group storytelling enables patients to discuss, understand and give personal meaning to the information that was exchanged, and facilitates educators' better understanding of patients' concerns and gaps in knowledge and how-to strategies that can inform their practice. The group storytelling intervention is acceptable to patients and educators and can be delivered with high fidelity. Further research into effective patient recruitment methods and evaluation of the intervention's impact on diabetes self-management is required.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".