[Health literacy and health education: what do these terms mean in the francophone context?].
Bibliographic record
Abstract
The objective of this exploratory study is to describe the nature of the definitions of health literacy and health education with the intention of contributing to the development of common semantics to enable professionals to exchange knowledge with less confusion. The methodology consisted of conducting a literature review of francophone databases using the following key words: literacy and education combined with the word health. The second phase of this research involved reading 126 articles found in francophone databases and collected from the Internet. The third included reading 14 documents focused on the relationship between literacy/education and health with the aim of defining each word and identifying their characteristics. The results show that francophone authors from North America used both terms, health literacy and health education, interchangeably; while European authors only refer to health education. Nonetheless, the analysis revealed that both terms have the same meaning on either side of the Atlantic, given that the nature of their definitions and their expected outcomes are the same. In conclusion, this study suggests that there is a need for an in-depth theoretical debate on these concepts in the francophone world and for stronger consideration of health literacy specifically targeting the community level, rather than the individual.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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".