Facilitating engagement through strong relationships between primary healthcare and Aboriginal and Torres Strait Islander peoples
Bibliographic record
Abstract
OBJECTIVE: Given the high prevalence of chronic disease, it is of concern that access to and sustained engagement with primary healthcare services by Aboriginal and Torres Strait Islander Australians is often far lower than would be expected. This study sought to explore ways in which relationships can support sustained engagement with healthcare services. METHODS: Semi-structured interviews were conducted with 126 Aboriginal and Torres Strait Islander participants with and without chronic disease and 97 Aboriginal and Torres Strait Islander and non-Indigenous healthcare providers, healthcare service managers or administrative staff. RESULTS: Our findings indicate that when faced with acute health issues, Aboriginal and Torres Strait Islander participants did prioritise care, provided that the service was both physically and emotionally welcoming. Trustworthiness of healthcare providers and strong relationships with patients were the most important factors for encouraging sustained engagement overtime. CONCLUSIONS: Responsibility for sustaining relationships does not rest solely with Aboriginal and Torres Strait Islander patients. Rather, healthcare providers need to commit to the process of building and maintaining relationships. IMPLICATIONS: First and foremost healthcare providers should take time to establish and then maintain relationships. Healthcare services can also contribute by ensuring facilities are welcoming for Aboriginal and Torres Strait Islander peoples.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".