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Record W4242521235 · doi:10.21203/rs.3.rs-34847/v1

A Conceptual Model of Functional Health Literacy to Improve Chronic Airway Disease Outcomes

2020· preprint· en· W4242521235 on OpenAlexaff
Iraj Poureslami, J Mark FitzGerald, Noah Tregobov, Jessica Shum, Alizeh Akhtar, Saron Kassay, Kassandra Starnes, Austin McMillan, Maryam Mahjob

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsConceptual modelDiseaseHealth literacyMedicineLiteracyPulmonary diseaseChronic diseaseIntensive care medicineAirwayPsychologyComputer scienceHealth carePolitical sciencePedagogyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0050.003
Science and technology studies0.0010.006
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.236
GPT teacher head0.561
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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