Adaptation transculturelle et tests psychométriques d’outils de mesure de l’efficacité personnelle et de l’adhésion thérapeutique pour une population d’adolescents diabétiques de type 1 français
Bibliographic record
Abstract
Introduction : many structured educational programs, using the concept of self-efficacy, have been studied in English-speaking countries. Background : tools were developed in English to assess this concept along with treatment adherence. However, there seems to be no French version of these tools in scientific literature. Aim : to adapt the tools to the French language and to test the psychometric properties of the Self-Efficacy for Diabetes Self-Management (SEDM) and the Diabetes Self-Management Profile (DSMP). Methods : a cross-cultural adaptation of the SEDM and DSMP in French was performed. The psychometric properties were tested in a pilot study that took place between January 1st and December 31st, 2015. Results : Cronbach’s alpha coefficient of SEDM in French was 0.84, test-retest reliability 0.80 and sensitivity to change was moderate. The Cronbach’s alpha and sensitivity to change of the French DSMP were low, and the test-retest was 0.71. Discussion and conclusions : the first results of the psychometric properties of French SEDM were rather encouraging. The use of the French version of DSMP seems compromised in terms of psychometric properties and the opinion of the participants.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".