Evaluation of a new performance-based, disease-specific health literacy measurement tool for patients with chronic airways diseases: the Vancouver Airways Health Literacy Tool (VAHLT)
Bibliographic record
Abstract
Abstract Background: Low health literacy (HL) is a global challenge. HL is positively correlated with chronic airways disease (CAD) outcomes. Despite the importance of HL in disease management, current HL measurement tools are suboptimal. As part of a multi-stage project to develop a performance-based, disease-specific Vancouver Airways Health Literacy Tool (VAHLT) for patients with CAD, this study assessed the relationships between VAHLT scores and CAD patient characteristics. The primary aim of the study is to provide preliminary evidence of construct validity of the VAHLT. Methods: A cross-sectional study design was applied. Patients were recruited from 6 specialty care clinics to complete the measurement tool. Demographic and clinical data, including quality of life (QOL) and disease control, were collected via validated questionnaires. Subjects also completed spirometry. Inferential analysis was conducted using mean difference testing and correlational methods. Results: 320 patients were recruited, and after imputing missing data, 315 were ultimately analyzed. Participants were predominantly female (60.5%), Caucasian (83.1%), had post high-school education (74.2%), with a mean age of 65.2 (SD = 13.17) years. Age was significantly negatively correlated with HL scores (p = .004); patients with post-high school education had significantly higher HL scores than those with a high school education or less (p < .001). No significant sex or ethnicity related differences in HL scores were observed. For clinical outcomes, no significant differences were found between HL scores and disease severity or measures of QOL and asthma control. Conclusions: We report a CAD-specific HL measurement tool developed with involvement of patients and professionals. Age and education were highly correlated with HL, which emphasizes the importance of addressing these factors in HL interventions among CAD patients. In the next phase, we will evaluate the tool’s responsiveness to interventions.
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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 | 0.005 | 0.016 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".