OPTIMISE: a pragmatic stepped wedge cluster randomised trial of an intervention to improve primary care for refugees in Australia
Bibliographic record
Abstract
OBJECTIVES: To examine whether primary care outreach facilitation improves the quality of care for general practice patients from refugee backgrounds. DESIGN: Pragmatic, cluster randomised controlled trial, with stepped wedge allocation to early or late intervention groups. SETTING, PARTICIPANTS: 31 general practices in three metropolitan areas of Sydney and Melbourne with high levels of refugee resettlement, November 2017 - August 2019. INTERVENTION: Trained facilitators made three visits to practices over six months, using structured action plans to help practice teams optimise routines of refugee care. MAJOR OUTCOME MEASURE: Change in proportion of patients from refugee backgrounds with documented health assessments (Medicare billing). Secondary outcomes were refugee status recording, interpreter use, and clinician-perceived difficulty in referring patients to appropriate dental, social, settlement, and mental health services. RESULTS: Our sample comprised 14 633 patients. The intervention was associated with an increase in the proportion of patients with Medicare-billed health assessments during the preceding six months, from 19.1% (95% CI, 18.6-19.5%) to 27.3% (95% CI, 26.7-27.9%; odds ratio, 1.88; 95% CI, 1.42-2.50). The impact of the intervention was greater in smaller practices, practices with larger proportions of patients from refugee backgrounds, recent training in refugee health care, or higher baseline provision of health assessments for such patients. There was no impact on refugee status recording, interpreter use increased modestly, and reported difficulties in refugee-specific referrals to social, settlement and dental services were reduced. CONCLUSIONS: Low intensity practice facilitation may improve some aspects of primary care for people from refugee backgrounds. Facilitators employed by local health services could support integrated approaches to enhancing the quality of primary care for this vulnerable population. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12618001970235 (retrospective).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".