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Association between health literacy and cardiac secondary prevention behaviours and health outcomes among culturally and linguistically diverse populations

2025· article· en· W7127653438 on OpenAlexaff
M Abou Chakra, Rebecca Jessup, A Beauchamp, D Azar, L Sharma, Stephen J. Nicholls, A Wong Shee, Andrea Driscoll, William J. van Gaal, Ernesto Oqueli, Jason Talevski

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsVictoria Heart Institute Foundation
Fundersnot available
KeywordsHealth literacyObservational studyScale (ratio)Logistic regressionAttendanceDescriptive statisticsLiteracyAnxiety

Abstract

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Abstract Introduction Culturally and linguistically diverse (CALD) populations can encounter challenges in managing coronary artery disease due to lower health literacy, cultural differences, and language barriers. These factors may impact adherence to recommended cardiac secondary prevention (CSP) interventions, such as attendance at cardiac rehabilitation. Purpose To explore associations between health literacy and CSP behaviours and health outcomes within CALD groups. Methods Data from a multicentre, prospective observational study (ENHEARTEN) conducted across three metropolitan and one regional hospital in Victoria, Australia were collected for 440 adults (>18 years) with their first myocardial infarction. The study explored associations between health literacy and CSP outcomes such as medication adherence, physical activity, cardiac rehabilitation, anxiety and depression. Data were collected via survey and medical records. Health literacy was measured using the 12-item European Health Literacy Survey (HLS-Q12) and four scales of the Health Literacy Questionnaire (HLQ): scale 3 (Actively managing my health), scale 4 (Social support for health), scale 6 (Engaging with healthcare providers) and scale 7 (Navigating the healthcare system). Descriptive statistics and univariate logistic regression were used to identify whether health literacy predicted CSP outcomes. Results CALD participants were more likely than non-CALD participants to have a higher level of education (10.4% vs. 5%; p<0.001), less likely to be a current smoker (29.4% vs. 36.5%; p=0.02), and were more likely to have high cholesterol (44.1% vs. 27.3%; p<0.01). Among CALD participants, higher scores on HLQ scales 6 and scale 7 were associated with improved medication adherence (OR=2.88, 95% CI:1.21, 6.90), (OR=2.16, 95% CI: 1.21, 3.86), respectively. Higher scores on HLQ scale 4 were associated with increased cardiac rehabilitation attendance in CALD participants (OR=2.69, 95% CI:1.21, 5.97). Higher scores on HLQ scales 4 were also associated with less likelihood of anxiety or depression (OR=0.21, 95% CI: 0.07, 0.61) (OR=0.29, 95% CI: 0.10, 0.86), respectively, as were higher scores on scale 6 (OR=0.37, 95% CI: 0.20, 0.72) (OR=0.43, 95% CI: 0.22, 0.84), and scale 7 (OR=0.46, 95% CI: 0.25, 0.84) (OR=0.40, 95% CI: 0.21, 0.77), respectively. No significant associations were found between health literacy and physical activity. And no significant associations were found between the HLS and CSP outcomes. Conclusion Higher health literacy in CALD participants was associated with improved CSP behaviours and health outcomes, including reduced depression and anxiety, greater medication adherence, and increased likelihood to attend cardiac rehabilitation. These findings highlight the importance of health literacy in managing cardiovascular health and suggest that targeted health literacy interventions may enhance secondary prevention and reduce health disparities in CALD populations.

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)0.003
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.483
Teacher spread0.393 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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