The Swiss Health Literacy Survey: development and psychometric properties of a multidimensional instrument to assess competencies for health
Bibliographic record
Abstract
BACKGROUND: Growing recognition of the role of citizens and patients in health and health care has placed a spotlight on health literacy and patient education. OBJECTIVE: To identify specific competencies for health in definitions of health literacy and patient-centred concepts and empirically test their dimensionality in the general population. METHODS: A thorough review of the literature on health literacy, self-management, patient empowerment, patient education and shared decision making revealed considerable conceptual overlap as competencies for health and identified a corpus of 30 generic competencies for health. A questionnaire containing 127 items covering the 30 competencies was fielded as a telephone interview in German, French and Italian among 1255 respondents randomly selected from the resident population in Switzerland. FINDINGS: Analyses with the software MPlus to model items with mixed response categories showed that the items do not load onto a single factor. Multifactorial models with good fit could be erected for each of five dimensions defined a priori and their corresponding competencies: information and knowledge (four competencies, 17 items), general cognitive skills (four competencies, 17 items), social roles (two competencies, seven items), medical management (four competencies, 27 items) and healthy lifestyle (two competencies, six items). Multiple indicators and multiple causes models identified problematic differential item functioning for only six items belonging to two competencies. CONCLUSIONS: The psychometric analyses of this instrument support broader conceptualization of health literacy not as a single competence but rather as a package of competencies for health.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".