A New Comprehensive Short-form Health Literacy Survey Tool for Patients in General
Bibliographic record
Abstract
PURPOSE: To validate a conceptual short-form health literacy 12 items questionnaire (HL-SF12) in patient populations. METHODS: A cross-sectional study was conducted via a convenient sample of 403 patients from three departments of a community general hospital in the northern Taiwan. Patients' health literacy was assessed with a validated HL-SF12, derived from the full scale, the European Health Literacy Survey Questionnaire (HLS-EU-Q), as well as a single-item from Chew's Set of Brief Health Literacy Question. A reference population in Northern Taiwan (n=928) via the HLS-EU-Q in 2013-2014 was used as a reference to compare the health literacy between that of the general public and the patients. Data was analyzed by confirmatory factor analysis (CFA), internal consistency analysis, correlation analysis, and linear regression models. RESULTS: Patients' health literacy assessed with the HL-SF12 was shown with high internal consistency (Cronbach α=.87), and moderately correlated with the single-item from Chew's Set of Brief Health Literacy Question, with satisfactory item-scale convergent validity (item-scale correlation ≥ .40), without floor/ceiling effect, and with satisfactory goodness of fit indices of the three-factor construct model for most of the patients. Their health literacy was significantly positively associated with female gender, higher income, and more often watching health-related TV programs. On the other hands, patients were reported with significantly higher healthcare health literacy than the general public, but not in general health literacy, disease prevention health literacy, or health promotion health literacy. CONCLUSION: The comprehensive HL-SF12 was a valid and easy to use tool for assessing patients' health literacy in the hospitals to facilitate healthcare providers in enhancing patients' health literacy and healthcare qualities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".