The Chronic Disease Self-Management Program: the experience of frequent users of health care services and peer leaders
Bibliographic record
Abstract
BACKGROUND: Large amount of evidence supports the contribution of the Stanford Chronic Disease Self-Management Program (CDSMP) to a global chronic disease management strategy. However, many studies have suggested further exploring of the factors influencing acceptance and completion of participants in this program. OBJECTIVE: This study aimed to describe and examine factors associated with acceptance and completion rates of the CDSMP among frequent users of health care services, and to highlight the experience of patients and peer leaders who facilitated the program. METHODS: A descriptive design with mixed sequential data was used. Acceptance and completion rates were calculated and their relationship with patient characteristics was examined in regression analysis (n = 167). Interviews were conducted among patients who accepted (n = 11) and refused (n = 13) to participate and with the program coordinator. Focus groups were held with the seven peer leaders who facilitated the program. Data were analysed using thematic analysis. RESULTS: Of the 167 patients invited, 60 (36%) accepted to participate in the program. Group format was the most frequent reason to decline the invitation to participate. Twenty-eight participants (47%) completed the program. Participants who dropped out during the program raised different reasons such as poor health and too much heterogeneity among participants. Factors such as location, schedule, content, group composition and facilitation were considered as important elements contributing to the success of the program. CONCLUSION: The CDSMP could therefore be considered as a self-management support option for this vulnerable clientele, while taking measures to avoid too much heterogeneity among participants to improve completion rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".