Introducing the chronic disease self-management program in Switzerland and other German-speaking countries: findings of a cross-border adaptation using a multiple-methods approach
Bibliographic record
Abstract
BACKGROUND: Stanford's Chronic Disease Self-Management Program (CDSMP) stands out as having a large evidence-base and being broadly disseminated across various countries. To date, neither evidence nor practice exists of its systematic adaptation into a German-speaking context. The objective of this paper is to describe the systematic German adaptation and implementation process of the CDSMP (2010-2014), report the language-specific adaptation of Franco-Canadian CDSMP for the French-speaking part of Switzerland and report findings from the initial evaluation process. METHODS: Multiple research methods were integrated to explore the perspective of workshop attendees, combining a longitudinal quantitative survey with self-report questionnaires, qualitative focus groups, and interviews. The evaluation process was conducted in for both the German and French adapted versions to gain insights into participants' experiences in the program and to evaluate its impact. Perceived self-efficacy was measured using the German version of the Self-Efficacy for Managing Chronic Disease 6-Item Scale (SES6G). RESULTS: Two hundred seventy eight people attending 35 workshops in Switzerland and Austria participated in the study. The study participants were receptive to the program content, peer-led approach and found principal methods useful, yet the structured approach did not address all their needs or expectations. Both short and long-term impact on self-efficacy were observed following the workshop participation (albeit with a minor decrease at 6-months). Participants reported positive impacts on aspects of coping and self-care, but limited effects on healthcare service utilization. CONCLUSIONS: Our findings suggest that the process for cross-border adaptation was effective, and that the CDSMP can successfully be implemented in diverse healthcare and community settings. The adapted CDSMP can be considered an asset for supporting self-management in both German-and French-speaking central European countries. It could have meaningful, wide-ranging implications for chronic illness care and primary prevention and potentially tertiary prevention of chronic disease. Further investigations are needed to tailor the program for better access to vulnerable and disadvantaged groups who might benefit the most, in terms of facilitating their health literacy in chronic illness.
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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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".