Barriers to accessing primary health care: comparing Australian experiences internationally
Bibliographic record
Abstract
Most highly developed economies have embarked on a process of primary health care (PHC) transformation. To provide evidence on how nations vary in terms of accessing PHC, the aim of this study is to describe the extent to which barriers to access were experienced by adults in Australia compared with other countries. Communities participating in an international research project on PHC access interventions were engaged to prioritise questions from the 2013 Commonwealth Fund International Health Policy Survey within a framework that conceptualises access across dimensions of approachability, acceptability, availability, affordability and appropriateness. Logistic regression models, with barriers to access as outcomes, found measures of availability to be a problematic dimension in Australia; 27% of adults experienced difficulties with out-of-hours access, which was higher than 5 of 10 comparator countries. Although less prevalent, affordability was also perceived as a substantial barrier; 16% of Australians said they had forgone health care due to cost in the previous year. After adjusting for age and health status, this barrier was more common in Australia than 7 of 10 countries. Findings of this integrated assessment of barriers to access offer insights for policymakers and researchers on Australia's international performance in this crucial PHC domain.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".