Home-Based, Peer-Led Chronic Illness Self-Management Training: Findings From a 1-Year Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: Studies suggest peer-led self-management training improves chronic illness outcomes by enhancing illness management self-efficacy. Limitations of most studies, however, include use of multiple outcome measures without predesignated primary outcomes and lack of randomized follow-up beyond 6 months. We conducted a 1-year randomized controlled trial of Homing in on Health (HIOH), a Chronic Disease Self-Management Program variant, addressing these limitations. METHODS: We randomized outpatients (N = 415) aged 40 years and older and who had 1 or more of 6 common chronic illnesses, plus functional impairment, to HIOH delivered in homes or by telephone for 6 weeks or to usual care. Primary outcomes were the Medical Outcomes Study 36-ltem short-form health survey's physical component (PCS-36) and mental component (MCS-36) summary scores. Secondary outcomes included the EuroQol EQ-5D and visual analog scale (EQ VAS), hospitalizations, and health care expenditures. RESULTS: Compared with usual care, HIOH delivered in the home led to significantly higher illness management self-efficacy at 6 weeks (effect size = 0.27; 95% CI, 0.10-0.43) and at 6 months (0.17; 95% CI, 0.01-0.33), but not at 1 year. In-home HIOH had no significant effects on PCS-36 or MCS-36 scores and led to improvement in only 1 secondary outcome, the EQ VAS (1-year effect size = 0.40; CI, 0.14-0.66). HIOH delivered by telephone had no significant effects on any outcomes. CONCLUSIONS: Despite leading to improvements in self-efficacy comparable to those in other CDSMP studies, in-home HIOH had a limited sustained effect on only 1 secondary health status measure and no effect on utilization. These findings question the cost-effectiveness of peer-led illness self-management training from the health system perspective.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".