Acceptability of general practice services for Afghan refugees in south-eastern Melbourne
Bibliographic record
Abstract
Over 750000 refugees have resettled in Australia since 1945. Despite complex health needs related to prior traumatic experiences and the challenges of resettlement in a foreign country, refugees experience poor access to primary care. Health and settlement service providers describe numerous cultural, communication, financial and health literacy barriers. This study aimed to investigate the acceptability of general practitioner (GP) services and understand what aspects of acceptability are relevant for Afghan refugees in south-eastern Melbourne. Semi-structured interviews were conducted with two Afghan community leaders and 16 Dari- or English-speaking Afghan refugees who accessed GP services. Two distinct narratives emerged - those of recently arrived refugees and established refugees (living in Australia for 3 years or longer). Transecting these narratives, participants indicated the importance of: (1) a preference for detailed clinical assessments, diagnostic investigations and the provision of prescriptions at the first consultation; (2) 'refugee-friendly' staff; and (3) integrated, 'one-stop-shop' GP clinic features. The value of acceptable personal characteristics evolved over time - GP acceptability was less a consideration for recently arrived, compared with more, established refugees. The findings reinforce the importance of tailoring healthcare delivery to the evolving needs and healthcare expectations of newly arrived and established refugees respectively.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".