Primary care intervention to address cardiovascular disease medication health literacy among Indigenous peoples: Canadian results of a pre-post-design study
Bibliographic record
Abstract
CONTEXT: Cardiovascular diseases (CVD) are a leading cause of illness and death for Indigenous people in Canada and globally. Appropriate medication can significantly improve health outcomes for persons diagnosed with CVD or for those at high risk of CVD. Poor health literacy has been identified as a major barrier that interferes with client understanding and taking of CVD medication. Strengthening health literacy within health services is particularly relevant in Indigenous contexts, where there are systemic barriers to accessing literacy skills. OBJECTIVE: The aim of this study is to test the effect of a customized, structured health literacy educational program addressing CVD medications. METHODS: Pre-post-design involves health providers and Indigenous clients at the De dwa da dehs nye>s Aboriginal Health Centre (DAHC) in Ontario, Canada. Forty-seven Indigenous clients with or at high risk of CVD received three educational sessions delivered by a trained Indigenous nurse over a 4- to 7-week period. A tablet application, pill card and booklet supported the sessions. Primary outcomes were knowledge of CVD medications and health literacy practices, which were assessed before and after the programe. RESULTS: Following the program compared to before, mean medication knowledge scores were 3.3 to 6.1 times higher for the four included CVD medications. Participants were also more likely to refer to the customized pill card and booklet for information and answer questions from others regarding CVD. CONCLUSIONS: This customized education program was highly effective in increasing medication knowledge and health literacy practice among Indigenous people with CVD or at risk of CVD attending the program at an urban Indigenous health centre.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".