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Record W3011671762 · doi:10.2147/copd.s234418

<p>Development and Pretesting of a New Functional-Based Health Literacy Measurement Tool for Chronic Obstructive Pulmonary Disease (COPD) and Asthma Management</p>

2020· article· en· W3011671762 on OpenAlexafffund
Iraj Poureslami, Jessica Shum, Jacek A. Kopec, Richard Sawatzky, Samir Gupta, Smita Pakhalé, Saron Kassay, Kassie Starnes, Alizeh Akhtar, J. Mark FitzGerald

Bibliographic record

VenueInternational Journal of COPD · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsOttawa HospitalUniversity of OttawaTrinity Western UniversitySt. Michael's HospitalWestern UniversityUniversity of British ColumbiaStornoway Diamond (Canada)Vancouver Coastal Health
FundersCanadian Institutes of Health Research
KeywordsMedicineHealth literacyAsthmaCOPDPulmonary rehabilitationFocus groupInhalerPhysical therapySelf-managementNumeracyLiteracyRehabilitationHealth careInternal medicinePsychology

Abstract

fetched live from OpenAlex

Background: Health literacy (HL) is a person's ability to practically apply a wide range of cognitive and non-cognitive skills in health-related decisions. HL includes five domains: navigate/access, understand, communicate, evaluate, and use of health information and services. Currently, no tool accurately captures and measures HL in adult patients with asthma and COPD, while utilizing all 5-HL domains. Objective: Develop a comprehensive functional-based measurement tool for adult asthma and/or COPD patients, while assessing HL on routine actions required to manage their chronic respiratory condition(s). Methods: We developed our HL tool based on a conceptualization of the link between HL and asthma and COPD management, during needs assessment stage including; a systematic review, which was followed by patient-oriented focus groups, and key-informant and respirologist interviews. Preliminary face and content validation were obtained by patients' and health professionals' input prior to the pretesting stage. The needs assessment information enabled us to develop passages in scenario-format and corresponding items to assess HL core domains, in addition to numeracy skills, across nine self-management topics: peak flow meters, prednisone use, pulmonary rehabilitation, action plans, flu shots, inhaler technique, lifestyle (nutrition and exercise), trigger control, and map navigation. The tool was pretested with asthma and COPD patients to assess its relevance, clarity, and difficulty. Results: Our systematic review identified the deficiencies of existing HL tools that assessed the HL skills of asthma and COPD patients. The patient-oriented focus groups (n=93) enabled us to identify self-management topics and develop items for our proposed HL tool, which were enriched by input from 45 key informants (eg, policy makers, clinicians, etc.) and 17 respiratory physicians. Preliminary pretesting with a new cohort of participants (36 asthma and COPD patients and 39 key informants) aided in the refinement and finalized our tool. The modified tool included passages and corresponding items related to asthma and COPD management was pretested with 75 asthma/COPD patients who completed the questionnaire and provided their feedback on the clarity, relevance, and difficulty of the tool. The main barrier to self-management pertained to "communication" skills. The flu shot was the most relevant topic (91.2%), while map navigation was the least relevant (63.9%). Action plans were the most difficult topic, where only 55% knew when to utilize their action plans. Numeracy items challenged COPD patients the most. Conclusion: We summarized findings from the development and preliminary testing stages of a new asthma/COPD HL tool. This tool will now be validated with a new cohort of patients. Practice Implications: Knowledge gained in this study has been applied to the final version of the tool, which is currently being validated.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.070
GPT teacher head0.381
Teacher spread0.311 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations16
Published2020
Admission routes2
Has abstractyes

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