Reaching those with the greatest need: how Australian primary health care service managers, practitioners and funders understand and respond to health inequity
Bibliographic record
Abstract
Equity of access to services and in health outcomes are key goals of primary health care. This study considers understandings of equity and perceptions of current performance in relation to equity among primary health care service staff, health service executives and funders. Semi-structured interviews were conducted with managers, practitioners and administration staff at five primary health care services in Adelaide and one in Alice Springs, as well as with South Australian funders and regional health service executives (n = 68). Services were responding to health inequity by taking actions to improve equitable access to their service, facilitating equitable access to health care more generally, and advocating and taking action on the social determinants of health inequities. As well as availability, affordability and acceptability, our analysis indicated a fourth dimension of equity of access we named 'engagement'. Our respondents were less able to point to examples of advocacy or action on the social determinants of health inequities than they were to examples of actions to improve equity of access. These findings indicate current strengths and also scope to encourage a broader and more comprehensive role for primary health care in addressing health inequities.
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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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".