A Conceptual Model of Functional Health Literacy to Improve Chronic Airway Disease Outcomes
Bibliographic record
Abstract
Abstract Background: Current conceptual models of health literacy (HL) illustrate the link between HL skills and health outcomes. However, these models fail to recognize and integrate certain elements of disease management, health system factors, and socio-demographic factors into a comprehensive framework. This article summarizes the process of developing a Chronic Airway Disease Management and Health Literacy (CADMaHL) conceptual model.Methods: The proposed CADMaHL model is developed within the following six stages: (1) Systematic review of HL measurement tools. (2) Patient-oriented focus group sessions. (3) HL and health professions (key-informants) interviews. (4) Attain perspectives and feedback of respirologists. (5) Develop a HL measurement tool for chronic airway disease (CAD) patients (e.g., asthma and COPD), pilot test, and tool modification. (6) Tool validation with asthma and COPD patients. Results: Throughout the study process, patient population groups, an advisory panel of HL experts, clinician scientists, and researchers reviewed the information acquired. This review process enabled us to organize the CADMaHL model into 6 primary modules, including INPUT, consists of four HL core components (access, understand, communicate, evaluate) and numeracy skill; OUTPUT, includes use/application of the obtained information; OUTCOME, covers patient empowerment in performing self-management practices by applying HL skills; ASSESSMENT, comprises HL assessment tools and strategies; IMPACT, includes mediators between HL and health outcomes; and CROSSCUTTING FACTORS, consists of diverse socio-demographics and health-system factors with applicability across the HL domains.Conclusions: We developed and validated the proposed HL tool using the CADMaHL model. We anticipate that the model may inform development of interventions aiming to improve HL and disease management outcomes of CAD patients, by properly identifying and mitigating HL gaps among these patient population groups.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".