The Self-Efficacy for Managing Chronic Disease Scale – French version: A validation study in primary care
Bibliographic record
Abstract
Rationale and objectiveThe evaluation of patient self-efficacy for managing chronic diseases is important in self-management education programs. A valid instrument to evaluate self-efficacy exists: the Self-Efficacy for Managing Chronic Disease 6-Item Scale (SEM-CD). The purpose of this study was to assess the psychometric properties of a French version of the instrument (SEM-CD Fv) in a primary health care context.MethodThe French translation of the questionnaire was obtained through a rigorous translation-back-translation process. Cronbach’s alpha, test-retest reliability (intra-class correlation coefficient, ICC), concurrent validity with the Skill and Technique Acquisition domain of the Health Education Impact Questionnaire (Heiq) and exploratory factor analysis were used to assess the psychometric properties of the SEM-CD Fv.ResultsWe analysed data from 326 primary care patients. The Cronbach alpha of the instrument was 0.93 (95% CI: 0.92 – 0.94). The ICC between the two administrations of the questionnaire (two-week interval) was 0.82 (95% CI: 0.69 – 0.90, p < 0.001). Concurrent validity of the SEM-CD Fv with the Skill and Technique Acquisition domain of the HeiQ showed a correlation coefficient of 0.49 (95% CI: 0.40 – 0.58, p < 0.001). Factor analysis for the SEM-CD Fv resulted in a one-factor solution that explained 73.8% of the variance.ConclusionThe SEM-CD Fv is a valid and reliable instrument to measure self-efficacy for managing chronic diseases in primary care patients.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".