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Record W2586954601 · doi:10.3917/rsi.127.0016

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

2017· article· fr· W2586954601 on OpenAlexaff
Sébastien Colson, José Côté, Madeleine Collombier, Christophe Debout, Galadriel Bonnel, Rachel Reynaud, Marie-Claude Lagouanelle-Simeoni

Bibliographic record

VenueRecherche en soins infirmiers · 2017
Typearticle
Languagefr
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPopulationMedicinePsychologyPhilosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.206
GPT teacher head0.476
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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