Examining the Support Peer Supporters Provide Using Structural Equation Modeling: Nondirective and Directive Support in Diabetes Management
Bibliographic record
Abstract
BACKGROUND: Little research has examined the characteristics of peer support. Pertinent to such examination may be characteristics such as the distinction between nondirective support (accepting recipients' feelings and cooperative with their plans) and directive (prescribing "correct" choices and feelings). PURPOSE: In a peer support program for individuals with diabetes, this study examined (a) whether the distinction between nondirective and directive support was reflected in participants' ratings of support provided by peer supporters and (b) how nondirective and directive support were related to depressive symptoms, diabetes distress, and Hemoglobin A1c (HbA1c). METHODS: Three hundred fourteen participants with type 2 diabetes provided data on depressive symptoms, diabetes distress, and HbA1c before and after a diabetes management intervention delivered by peer supporters. At post-intervention, participants reported how the support provided by peer supporters was nondirective or directive. Confirmatory factor analysis (CFA), correlation analyses, and structural equation modeling examined the relationships among reports of nondirective and directive support, depressive symptoms, diabetes distress, and measured HbA1c. RESULTS: CFA confirmed the factor structure distinguishing between nondirective and directive support in participants' reports of support delivered by peer supporters. Controlling for demographic factors, baseline clinical values, and site, structural equation models indicated that at post-intervention, participants' reports of nondirective support were significantly associated with lower, while reports of directive support were significantly associated with greater depressive symptoms, altogether (with control variables) accounting for 51% of the variance in depressive symptoms. CONCLUSIONS: Peer supporters' nondirective support was associated with lower, but directive support was associated with greater depressive symptoms.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".