Predictors and Effects of Participation in Peer Support: A Prospective Structural Equation Modeling Analysis
Bibliographic record
Abstract
BACKGROUND: Peer support provides varied health benefits, but how it achieves these benefits is not well understood. PURPOSE: Examine a) predictors of participation in peer support interventions for diabetes management, and b) relationship between participation and glycemic control. METHODS: Seven peer support interventions funded through Peers for Progress provided pre/post data on 1,746 participants' glycemic control (hemoglobin A1c), contacts with peer supporters as an indicator of participation, health literacy, availability/satisfaction with support for diabetes management from family and clinical team, quality of life (EQ-Index), diabetes distress, depression (PHQ-8), BMI, gender, age, education, and years with diabetes. RESULTS: Structural equation modeling indicated a) lower levels of available support for diabetes management, higher depression scores, and older age predicted more contacts with peer supporters, and b) more contacts predicted lower levels of final HbA1c as did lower baseline levels of BMI and diabetes distress and fewer years living with diabetes. Parallel effects of contacts on HbA1c, although not statistically significant, were observed among those with baseline HbA1c values > 7.5% or > 9%. Additionally, no, low, moderate, and high contacts showed a significant linear, dose-response relationship with final HbA1c. Baseline and covariate-adjusted, final HbA1c was 8.18% versus 7.86% for those with no versus high contacts. CONCLUSIONS: Peer support reached/benefitted those at greater disadvantage. Less social support for dealing with diabetes and higher PHQ-8 scores predicted greater participation in peer support. Participation in turn predicted lower HbA1c across levels of baseline HbA1c, and in a dose-response relationship across levels of participation.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".