Implementing a peer-support intervention for people with type 2 diabetes: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: This qualitative study aimed to explore participants', peer supporters' and practice nurses' experience of the implementation of a peer-support intervention for people with type 2 diabetes. METHODS: The study was conducted in family practice in Ireland. Participants were selected from the patients, peer supporters and practice nurses who participated in a 2-year randomized controlled trial of peer support in type 2 diabetes. The sample consisted of 6 practice nurses, 15 peer supporters and 33 intervention participants. Data were collected using focus groups and semi-structured interviews and transcribed verbatim. Key themes and concepts were identified using framework analysis. RESULTS: The following themes emerged: who gets invited to be a peer supporter?; training and support for peer supporters; and peer-support meetings and challenges of delivering a peer-support programme. Recruiting peer supporters via the general practices was successful. Although some peer supporters were hesitant to participate initially, they were satisfied in their role and felt well trained and supported. Participants were overwhelmingly positive about the peer-support meetings. They welcomed the fact that the meetings were led by a peer; however, some participants reported that they would have liked occasional input from health professionals. The 'Frequently Asked Questions' element of the intervention was very popular with both participants and peer supporters. CONCLUSIONS: This study revealed that it was feasible to implement a peer-support intervention in the general practice setting. Challenges of delivering such an intervention were identified, particularly in relation to meeting attendance, and should be considered in further research in the area.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".