An Integrated Framework to Conceptualize and Develop the Vancouver Airways Health Literacy Tool (VAHLT)
Bibliographic record
Abstract
There is currently no comprehensive tool to assess the functional health literacy (HL) skills of chronic airway disease (CAD) patients. The purpose of this article is to describe the development of a new HL measure, the Vancouver Airways Health Literacy Tool (VAHLT). The tool was developed through the following phases: (1) Tool conceptualization, consisting of: (A) a systematic review (SR), (B) focus group sessions with CAD patients to understand barriers and facilitators to CAD management, (C) a survey with key-informants to obtain strategies to mitigate self-management barriers and validate patient-derived topics, and (D) respiratory physicians’ review of the topics; (2) Scenario and item development; and (3) Tool testing and content validation. The SR identified the lack of a valid HL measurement tool for CAD patients. Patients provided an initial shortlist of disease-related self-care topics. Key-informants helped to finalize topics for inclusion. Respiratory physicians and patients contributed to the development of a scenario-based questionnaire, which was refined during three rounds of testing to develop a 44-item instrument comprising nine self-management passages. We highlight the holistic process of integrating information from the literature with knowledge gained from key stakeholders into our tool framework. Our approach to stakeholder engagement may be of interest to researchers developing similar tools, and could facilitate the development and testing of HL-based interventions to ultimately improve patient outcomes and reduce the burden on the healthcare system.
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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 | 0.042 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".