Measuring health literacy in Europe: Introducing the European Health Literacy Survey Questionnaire (HLS-EU-Q)
Bibliographic record
Abstract
At the beginning of the millennium, the growing interest and concerns regarding the impact of limited health literacy in North America was recognised, and health literacy was brought up among European politicians and researchers as being of relevance for active health citizenship and patient participation in contrast to the more prevailing paternalistic views. However, no European population data on health literacy existed, and it became evident that more information was needed to inform the policy discussions (Sørensen and Brand, 2017). Compared to the US, Canada and Australia, measuring health literacy not only came to Europe rather late, but measurement also followed quite a different approach. While in the US, after few studies in the tradition of population literacy measurement – using, for example, the Health and Literacy Scale (HALS) – the bulk of health literacy studies focused on the consequences of the low clinical health literacy of patient populations, using for measurement (rather short) instruments of functional health literacy (Rudd, 2017), in Europe, measurement started with a rather broad concept of health literacy in general populations (Sørensen et al, 2012; Wang et al, 2012; Pelikan and Ganahl, 2017a, b). Crucial for the European developments were Ilona Kickbusch (Kickbusch, 2001, 2002; Kickbusch et al, 2006; Kickbusch and Maag, 2008) and Don Nutbeam (Nutbeam, 2000; Nutbeam and Kickbusch, 2000), who had recognised the potential of health literacy for health promotion and public health, besides its importance for healthcare (see Chapter 2, this volume). From 2004 onwards, Ilona Kickbusch advocated for health literacy within the European Health Forum Gastein (Kickbusch, 2004), and initiated the HLS-CH study in Switzerland in 2006 (Wang et al, 2012).
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".