Development and validation of a new comprehensive measurement tool for health insurance literacy
Bibliographic record
Abstract
BACKGROUND: Health insurance literacy (HIL) reflects individuals' ability to understand, select, and effectively use health insurance, impacting healthcare access and utilization. Existing measurement tools often lack comprehensiveness or contextual relevance. This study aimed to develop a comprehensive questionnaire for measuring HIL, encompassing all recognized dimensions and expanding upon the Health Insurance Literacy Measure (HILM), while also adapting it to the specific cultural and national context, using Israel as a case study and proof of concept. METHODS: A multi-phase methodology was employed to develop a comprehensive HIL questionnaire, including an extensive literature review, expert consultations, and iterative pilot testing to ensure cultural and contextual relevance. Exploratory factor analysis (EFA) and reliability testing were conducted using data from a representative sample of 1,012 adults to validate its psychometric properties. RESULTS: A 75-item questionnaire was designed, covering four domains: confidence and behavior in choosing and using health insurance (HILM), self-report confidence in understanding of key insurance concepts, objective knowledge assessment, and self-assessment of HIL. The questionnaire employs a combination of Likert-type scales and binary scoring for objective knowledge items. EFA confirmed a robust multidimensional structure. The final model accounted for 61% of the variance in the confidence and behavior domains and 57% in the concept domain, while the objective knowledge domain showed less definitive factor loadings. Internal consistency was high across all domains (Cronbach's alpha = 0.80-0.95), and concurrent and convergent validity analyses demonstrated moderate to strong correlations with external measures of understanding and self-assessed knowledge, supporting its psychometric robustness. CONCLUSIONS: This validated questionnaire presents a robust, culturally adapted measure of HIL, integrating both objective knowledge and subjective confidence, offering insights into the multidimensional nature of HIL. It provides critical insights for policymakers and educators aiming to enhance public understanding and effective use of health insurance, setting the stage for targeted interventions and broader international applications. TRIAL REGISTRATION: Not applicable.
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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 | 0.028 | 0.040 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".