Rethinking peer support for diabetes in Vancouver's South‐Asian community: a feasibility study
Bibliographic record
Abstract
AIM: To examine the feasibility and potential health impact of a diabetes self-management education and support intervention involving peer support on glycaemic control and diabetes distress. METHODS: A total of 41 South-Asian adults with Type 2 diabetes were recruited for a 24-week diabetes self-management education and support pilot intervention involving peer support. The intervention consisted of six weekly education sessions co-facilitated by a certified diabetes educator and two peer leaders, followed by 18 weekly support sessions facilitated by two peer leaders. Education sessions were guided entirely by participants' self-management questions and also emphasized goal setting and action planning. Support sessions were based on empowerment principles and participants discussed self-management challenges, shared emotions, asked self-management questions, problem-solved in a group, set goals, and developed and evaluated action plans. Feasibility outcomes included recruitment and retention. Primary health-related outcomes included HbA1c levels and diabetes distress (measured at baseline, 6 and 24 weeks). Programme satisfaction was also assessed. RESULTS: Pre-established criteria for recruitment and retention were met. Paired t-tests showed no changes in HbA1c and diabetes distress at 6 weeks. At 24 weeks, HbA1c levels deteriorated [54 mmol/mol (7.1%) vs 61 mmol/mol (7.7%)] while diabetes distress scores improved (2.0 vs 1.7). CONCLUSIONS: Although feasible, findings suggest this peer-support model may have a positive impact on diabetes distress, but not on HbA1c levels. Culturally responsive modifications (e.g. intervention location) to the pilot model are needed and could lead to more favourable health outcomes for this community. Such a re-designed peer-support model will require further investigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".