Uncovering health literacy: Developing a remotely administered questionnaire for determining health literacy levels in health disparate populations
Bibliographic record
Abstract
INTRODUCTION: Low health literacy contributes to health disparities. We sought to develop and evaluate a remotely administered tool to measure health literacy in health disparate populations. The basic research design involved asking the remotely administered questions in conjunction with an existing and valid measure of health literacy, the S-TOFHLA, to a non-representative convenience sample of individuals drawn from lower income communities. The measures of the remotely administered questions were then correlated with the results of the S-TOFHLA to determine if there was a connection between the two measures. We found a statistically significant correlation between a single question in the remotely administered survey and the validated S-TOFHLA measure. This research supports previous work that points to the importance of just a single remotely administered question in terms of correspondence with the S-TOFHLA. OBJECTIVE: Develop a questionnaire that can be remotely administered to check for Health Literacy. METHODS: Correlation analysis is conducted between various questions and S-TOFHLA scores to determine criterion validity. RESULTS: A single question, "How confident are you in filling out medical forms by yourself?" outperforms other measures in correlating with the S-TOFHLA scores. CONCLUSIONS: Further assessment of the confidence question both in isolation and in conjunction with other literacy identifiers should be conducted. Also, this question should be tested against other measures of health literacy beyond the S-TOFHLA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".