Validation d’un questionnaire sur l’alphabétisme en santé des francophones en situation linguistique minoritaire
Bibliographic record
Abstract
OBJECTIVE: This study was designed to develop and test the qualitative validity of a French questionnaire. The questionnaire explored the mechanisms by which Francophone in situation of linguistic minority mobilize their social capital and cultural assets in terms of health literacy. METHODS: We used evidence derived from a previous qualitative study in which we elaborated the characteristics of social capital and cultural assets. Drawing on models of social capital and self-determination of language behaviour in Francophone in situation of linguistic minority, 21 items were developed and assessed to confirm content validity. For this purpose, cognitive interviews were conducted with 92 natural experts. These cognitive interviews tested the linguistic and content validity of the instrument. Consultations with research professionals were also conducted to increase content validity. RESULTS: Parsimony and simplicity were tested by application of the Flesch test. The results demonstrate that 19 questions (93%) corresponded to the general public, relatively simple, and everyday use categories. Natural experts confirmed the readability and simplicity of the questionnaire. CONCLUSION: This study addressed a lack of simple and readable questionnaire for collecting data among Francophone in situation of linguistic minority. The findings underscore the lack of evidence related to the familiar construction of health literacy among members of this population.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".