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 and health outcomes. However, these models fail to recognize and integrate certain elements of disease management, health system factors, and socio-demographic factors into their framework. This article outlines the development of Chronic Airway Disease (CAD) Management and Health Literacy (CADMaHL) conceptual model that integrates the aforementioned elements and factors into a single framework.Methods: Information obtained during the following stages informed the development of our model: (1) a systematic review of existing CAD HL measurement tools that apply core HL domains; (2) patient-oriented focus group sessions to understand HL barriers to CAD self-management practices; (3) key-informant interviews to obtain potential strategies to mitigate CAD management barriers, and validate disease self-management topics; (4) elicited the perspectives of Canadian respirologist’s on the ideal functional HL skills for asthma and COPD patients.Results: Throughout the study process many stakeholders (i.e., patients, key-informants, and an international HL advisory panel) contributed to and reviewed the model. The process enabled us to organize the CADMaHL model into 6 primary modules, including: INPUT, consisting of four HL core components (access, understand, communicate, evaluate,) and numeracy skills; OUTPUT, including application of the obtained information; OUTCOME, covering patient empowerment in performing self-management practices by applying HL skills; ASSESSMENT, consisting of information about functionality and relevancy of CADMaHL; IMPACT, including mediators between HL and health outcomes; CROSSCUTTING FACTORS, consisting of diverse socio-demographics and health-system factors with applicability across the HL domains. Conclusions: We developed the CADMaHL model, with input from key-stakeholders, which addresses a knowledge gap by integrating various disease management, health-system and socio-demographic factors absent from previous published frameworks. We anticipate that our model will serve as the backbone for the development of a comprehensive HL measurement tool, which may be utilized for future HL interventions for CAD patients. Trial Registration Number: NCT01474928- Date of registration: 11/26/2017
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".