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Record W4226482518

Factors Affecting the Reception of Self-Management Health Education: A Cross-Sectional Survey Assessing Perspectives of Lower-Income Seniors with Cardiovascular Conditions

2022· article· en· W4226482518 on OpenAlexaffabout
Sophia Tran, Robert G. Weaver, Braden Manns, Terry Saunders‐Smith, Tavis S. Campbell, Noah Ivers, Marcello Tonelli, Raj Pannu, David J.T. Campbell

Bibliographic record

VenueDove Medical Press (Taylor and Francis Group) · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of AlbertaUniversity of CalgaryWomen's College HospitalUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsHelpfulnessMedicineCopaymentReceiptPoisson regressionCross-sectional studyFamily medicineIntervention (counseling)Environmental healthNursingHealth careAccounting
DOInot available

Abstract

fetched live from OpenAlex

Sophia HN Tran,1,2 Robert G Weaver,2 Braden J Manns,2,3 Terry Saunders-Smith,2 Tavis Campbell,4 Noah Ivers,5,6 Brenda R Hemmelgarn,7 Marcello Tonelli,2,3 Raj Pannu,8 David JT Campbell2,3,9 1Department of Psychology, University of Waterloo, Waterloo, ON, Canada; 2Department of Medicine, University of Calgary, Calgary, AB, Canada; 3Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; 4Department of Psychology, University of Calgary, Calgary, AB, Canada; 5Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada; 6Department of Family and Community Medicine, Women’s College Hospital, Toronto, ON, Canada; 7Faculty of Medicine & Dentistry, University of Alberta, Calgary, AB, Canada; 8Emergence Creative, New York, NY, USA; 9Department of Cardiac Sciences, University of Calgary, Calgary, AB, CanadaCorrespondence: David JT Campbell, Tel +1 403-210-9511, Email dcampbel@ucalgary.caIntroduction: Self-management education and support (SMES) programs can prevent adverse chronic disease outcomes, but factors modifying their reception remain relatively unexplored. We examined how perceptions of an SMES program were influenced by the mode of delivery, and co-receipt of a paired financial benefit.Methods and Patients: Using a cross-sectional survey, we evaluated the perceived helpfulness of a SMES program among 446 low-income seniors at high risk for cardiovascular events in Alberta, Canada. Secondary outcomes included frequency of use, changes in perspectives on health, satisfaction with the program, and comprehensibility of the material. Participants received surveys after engaging with the program for at least 6 months. We used modified Poisson regression to calculate relative risks. Open-ended questions were analyzed inductively.Results: The majority of participants reported that the SMES program was helpful (> 80%). Those who also received the financial benefit (elimination of medication copayments) were more likely to report that the SMES program was helpful (RR 1.24, 95% CI 1.11– 1.39). Those who received the program electronically were more likely to use the program weekly (RR 1.51, 1.25– 1.84). Both those who received the intervention electronically (RR 1.18, 1.06– 1.33), and those who also received copayment elimination (RR 1.17, 1.05– 1.31) were more likely to state that the program helped change their perspectives on health.Conclusion: When designing SMES programs, providing the option for electronic delivery appears to promote greater use for seniors. The inclusion of online-delivery and co-receipt of tangible benefits when designing an SMES program for seniors results in favorable reception and could facilitate sustained adherence to health behavior recommendations. Participants also specifically expressed that what they enjoyed most was that the SMES program was informative, helpful, engaging, and supportive.Keywords: self-management, chronic disease, tailored programs, educational intervention, cardiovascular prevention

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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 designObservational
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

Citations6
Published2022
Admission routes2
Has abstractyes

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