Factors influencing health care utilisation among Aboriginal cardiac patients in central Australia: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Aboriginal Australians suffer from poorer overall health compared to the general Australian population, particularly in terms of cardiovascular disease and prognosis following a cardiac event. Despite such disparities, Aboriginal Australians utilise health care services at much lower rates than the general population. Improving health care utilisation (HCU) among Aboriginal cardiac patients requires a better understanding of the factors that constrain or facilitate use. The study aimed to identify ecological factors influencing health care utilisation (HCU) for Aboriginal cardiac patients, from the time of their cardiac event to 6-12 months post-event, in central Australia. METHODS: This qualitative descriptive study was guided by an ecological framework. A culturally-sensitive illness narrative focusing on Aboriginal cardiac patients' "typical" journey guided focus groups and semi-structured interviews with Aboriginal cardiac patients, non-cardiac community members, health care providers and community researchers. Analysis utilised a thematic conceptual matrix and mixed coding method. Themes were categorised into Predisposing, Enabling, Need and Reinforcing factors and identified at Individual, Interpersonal, Primary Care and Hospital System levels. RESULTS: Compelling barriers to HCU identified at the Primary Care and Hospital System levels included communication, organisation and racism. Individual level factors related to HCU included language, knowledge of illness, perceived need and past experiences. Given these individual and health system barriers patients were reliant on utilising alternate family-level supports at the Interpersonal level to enable their journey. CONCLUSION: Aboriginal cardiac patients face significant barriers to HCU, resulting in sub-optimal quality of care, placing them at risk for subsequent cardiovascular events and negative health outcomes. To facilitate HCU amongst Aboriginal people, strategies must be implemented to improve communication on all levels and reduce systemic barriers operating within the health system.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".