Cardiovascular disease medication health literacy among Indigenous peoples: design and protocol of an intervention trial in Indigenous primary care services
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVD) are leading causes of mortality and morbidity among Indigenous people in New Zealand, Australia and Canada and are a major driver of the inequities in life expectancy between Indigenous and non-Indigenous people in these countries. Evidence-based pharmaceutical management of CVD can significantly reduce mortality and morbidity for persons diagnosed with CVD or for those at intermediate or high risk of CVD. Health literacy has been identified as a major barrier in the communication and implementation of appropriate pharmaceutical management plans for CVD. Addressing health literacy is particularly relevant in Indigenous populations where there are unique health and adult literacy challenges. METHODS/DESIGN: This study will examine the effect of a customized, structured CVD medication programme, delivered by health professionals, on the health literacy of Indigenous people with, or at risk, of CVD. Primary outcomes are patient's knowledge about CVD medications; secondary outcomes examine changes in health literacy skills and practices. The study will employ a multi-site pre-post design with multiple measurement points to assess intervention efficacy. Participants will be recruited from four Indigenous primary care services in Australia, Canada and New Zealand. Three educational sessions will be delivered over four weeks. A tablet application will support the education sessions and produce a customized pill card for each participant. Participants will be provided with written information about CVD medications. Medication knowledge scores, and specific health literacy skills and practices will be assessed before and after the three sessions. Statistical analyses will identify significant changes in outcomes over each session, and from the pre-session one to post-session three time points. DISCUSSION: This study will make an important contribution to understanding the effect of a structured primary care-based intervention on CVD health literacy in Indigenous populations. The study also illustrates the incorporation of Indigenous health research principles and processes in clinical trials and provides insights that may be useful in other contexts. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Register (ACTRN12612001309875; date of registration 18/12/2012).
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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 | 0.046 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".