Measuring navigational health literacy – an extension of the HLS19-NAV scale
Bibliographic record
Abstract
Abstract Background European healthcare systems are characterized by a high complexity and intransparency. Finding one’s way through the multitude of services requires navigational health literacy (NAV-HL). NAV-HL is defined as the ability to maneuver the healthcare system and deal with information necessary to do so at the system, organizational, and interactive levels. Using a newly developed scale, the European health literacy population survey 2019-2021 (HLS19) measured NAV-HL across multiple countries. To better capture the complexity of the construct and enhance scale validity, the aim was to improve it by generating additional items on the interactive level. Methods An international panel of health literacy experts from ten countries generated additional items that specifically covered tasks related to interactions with healthcare professionals. These skills are important for ensuring continuity of healthcare and navigating the healthcare system. The items were generated in an iterative process, translated, and tested in cognitive interviews. Results Four items were generated according to the four cognitive domains of the underlying conceptual model. These items concern the proficiency to: a) obtain information from health-care professionals about further healthcare services, b) understand this information and c) assess it in relation to one’s own preferences, and d) use it to make decisions about further healthcare. Using cognitive interviews, the four new items have been found to be meaningful and comprehensible. Only minor adjustments were made. Conclusions The HLS19-NAV scale has already been successfully applied and validated in eight countries of the HLS19. The new items capturing the interactive level enhance the validity of the existing HLS19-NAV scale. The psychometric properties of the new (sub)scale will be explored.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".