Providing guideline-recommended preventive cardiovascular care to Aboriginal and Torres Strait Islander women: exploring gender differences with a medical record review in primary health care
Bibliographic record
Abstract
BACKGROUND: For Aboriginal and Torres Strait Islander women, the premature burden of cardiovascular disease is affecting their capacity to fulfil roles in society, and promote the health and wellbeing of future generations. In Australia, there is limited understanding of the difference in primary preventive cardiovascular care experienced by women, despite knowledge of sex and gender differentials in health profile and receipt of guideline-based acute care. This paper sought to explore the health profile and receipt of assessment and management of cardiovascular risk for Aboriginal and Torres Strait Islander women accessing preventive primary health care, and investigate gender differentials. METHOD: Records of 1200 current clients, 50% women, aged 18-74years from three Aboriginal Health Services in central and South Australia for the period 7/2018-6/2020 were reviewed. RESULTS: Twelve percent had documented cardiovascular disease. Compared with men, women with no recorded cardiovascular disease had a greater likelihood of being overweight or obese, a waist circumference indicative of risk, diabetes, and depression. Women were less likely to report being physically active. CONCLUSIONS: The research concluded that gaps exist in the provision and recording of guideline-recommended primary preventive care regardless of sex. These are stark, given the evident burden.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".