Depressive Symptoms Moderated the Effect of Chronic Illness Self-Management Training on Self-Efficacy
Bibliographic record
Abstract
BACKGROUND: Identifying moderators of the effects of self-efficacy enhancing interventions could facilitate their refinement and more targeted, cost-effective delivery. Current theories and data concerning the potential moderating effect of depressive symptoms on interventions to enhance patient chronic illness self-management self-efficacy are conflicting. OBJECTIVES: To explore the moderating effect of depressive symptoms on the effect of an intervention to enhance patient self-efficacy for self-managing chronic illness. RESEARCH DESIGN: Regression analyses using baseline and postintervention (6 weeks) data from an ongoing randomized controlled trial. SUBJECTS: Patients (N = 415) aged >or=40 years recruited from a primary care network in Northern California with arthritis, asthma, chronic obstructive pulmonary disease, congestive heart failure, depression, and/or diabetes mellitus, plus impairment in >or=1 basic activity, and/or a score of >or=4 on the 10-item Center for Epidemiologic Studies Depression Scale (CES-D). MEASURES: Stanford self-efficacy scale, self-reported depression, CES-D, and Medical Outcomes Study Short Form health status questionnaire (SF-36) Mental Component Summary score. RESULTS: Regression analyses revealed the intervention was effective primarily in those with self-reported depression (interaction effect F = 8.24, P = 0.0003), highest CES-D score category (F = 5.68, P = 0.0037), and lowest (most depressed) Mental Component Summary-36 tercile (F = 4.36, P = 0.0135). CONCLUSIONS: Individuals with more depressive symptoms seem more likely to experience self-efficacy gains from chronic illness self-management training than individuals with less depressive symptoms. Future self-management training studies should stratify subjects within study groups by depressive symptom level to further explore its potential moderating effect.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".