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Record W4210355383 · doi:10.4103/hm.hm_64_21

Socioeconomic and Clinical Factors Associated with Disease-Related Knowledge of Cardiac Rehabilitation Patients in Brazil

2022· article· en· W4210355383 on OpenAlexaff
Jessica B. Loures, Gabriela Chaves, Renata C. Ribas, Raquel Rodrigues Britto, Marian Marchiori, Gabriela L. Melo Ghisi

Bibliographic record

VenueHeart and Mind · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsSocioeconomic statusMedicineDiseasePsychological interventionRehabilitationFamily incomeOccupational prestigePhysical therapyEnvironmental healthPopulationInternal medicineNursing

Abstract

fetched live from OpenAlex

Objective: The objective of this study was to identify socioeconomic and clinical factors associated with disease-related knowledge of cardiac rehabilitation (CR) patients. Methods: Adults with coronary artery disease (CAD) were recruited during CR Phase 1 and completed questionnaires on the 1 st day of Phase 2. Disease-related knowledge was assessed by the short version of the CAD Education Questionnaire. Socioeconomic status was defined by educational level, family income, and employment status. MannWhitney U and Spearman correlation were calculated to determine the association of knowledge with socioeconomic factors, number of risk factors, and wait time between hospital discharge and start of outpatient CR. Results: A convenience sample of 39 patients were recruited. Overall, the mean knowledge was 12.00 ± 3.3, which corresponds to 60% of possible scores. Monthly family income and number of risk factors influenced medical condition knowledge ( P < 0.05), and employment status influenced total knowledge ( P = 0.005) and risk factor knowledge ( P = 0.002). Participants with three or more risk factors presented significantly higher knowledge ( P = 0.02). Those that waited more than 17 weeks to start the CR presented significantly lower knowledge ( P = 0.04). Conclusion: Participants with low income and unemployed were more likely to have inadequate disease-related knowledge; however, the entire sample presented low understanding of their condition. Public health strategies and educational interventions must continue to focus on these vulnerable 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.003
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.342
Teacher spread0.324 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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