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Record W4411134491 · doi:10.7202/1118272ar

L’adaptation d’une échelle composite d’évaluation des comportements de promotion de la santé pour les adolescents et les jeunes adultes

2024· article· fr· W4411134491 on OpenAlexvenueno aff
Gustave Soh, André Wamba, Mei‐Yen Chen

Bibliographic record

VenueMesure et évaluation en éducation · 2024
Typearticle
Languagefr
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

Les outils d’évaluation des comportements de promotion de la santé abondent en anglais alors qu’ils sont rares, voire quasi-inexistants en français. Cet article vise à valider un questionnaire composite d’identification des comportements pro-santé ou à risque. Les dix étapes de l’adaptation des tests/échelles de mesure psychologique de Gana et al. (2021) ont été appliquées aux données collectées auprès de 343 adolescents et jeunes adultes de 15-25 ans (moyenne = 17,6 ± 1,76) ayant rempli un questionnaire de 51 items issus de trois échelles préexistantes. Les analyses factorielles exploratoires et confirmatoires supportent la structure à huit dimensions avec 27 items expliquant 56,69 % des variances : santé spirituelle, relation interpersonnelle, exercice physique, gestion du stress, appréciation de la vie, hygiène du sommeil, responsabilité en santé et soutien social. Le questionnaire composite d’évaluation des comportements de promotion de santé (AHPB-27) est donc valide pour identifier des comportements de promotion de la santé des adolescents et des jeunes adultes en francophonie.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.136
GPT teacher head0.495
Teacher spread0.359 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

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