The utilisation of public and private health care among Australian women with diabetes: Findings from the 45 and Up Study
Bibliographic record
Abstract
AIM: To describe the prevalence of health care utilisation and out-of-pocket expenditure associated with the management of diabetes among Australian women aged 45 years and older. DESIGN: Cross-sectional survey design. METHODS: The questionnaire was administered to 392 women (a cohort of the 45 and Up Study) reporting a diagnosis of diabetes between August and November 2016. It asked about the use of conventional medicine, complementary medicine (CM) and self-prescribed treatments for diabetes and associated out-of-pocket spending. RESULTS: Most women (88.3%; n = 346) consulted at least one health care practitioner in the previous 12 months for their diabetes; 84.6% (n = 332) consulted a doctor, 44.4% (n = 174) consulted an allied health practitioner, and 20.4% (n = 80) consulted a CM practitioner. On average, the combined annual out-of-pocket health care expenditure was AU$492.6 per woman, which extrapolated to approximately AU$252 million per annum. Of this total figure, approximately AU$70 million was spent on CM per annum. CONCLUSIONS: Women with diabetes use a diverse range of health services and incur significant out-of-pocket expense to manage their health. The degree to which the health care services women received were coordinated, or addressed their needs and preferences, warrants further exploration. Limitations of this study include the use of self-report and inability to generalise findings to other populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".