Demographics and utilisation of health services by paediatric refugees from East Africa: implications for service planning and provision
Bibliographic record
Abstract
Regina Cooke is a Clinical Fellow at the Royal Children's Hospital, Melbourne. Sally Murray is an Honorary Fellow of the University of Melbourne and former Program Coordinator of the Victorian Immigrant Health Program, Department of Paediatrics, University of Melbourne. Jonathan Carapetis is an Infectious Diseases Physician, Royal Children's Hospital, Senior Lecturer, Department of Paediatrics,University of Melbourne and Research Fellow, Murdoch Children's Research Institute. James Rice is a Clinical Fellow at University of British Columbia, Canada and formerly of Royal Children's Hospital, Melbourne. Nigisti Mulholland is a Social Scientist, formerly of Royal Children's Hospital, Melbourne.Susan Skull is Deputy Director of the Clinical Epidemiology and Biostatistics Unit, Royal Children's Hospital, and Senior Lecturer, Department of Paediatrics, University of Melbourne.Little is known of difficulties in accessing health care for recently arrived paediatric refugees in Australia. We reviewedroutinely collected data for all 199 East African children attending a hospital Immigrant Health Clinic for the first time over a 16 month period. Although 63% of parents reported medical consultations since arrival, 77% of this group reported outstanding, unaddressed health problems. Availability of interpreters and information on health services were the main factors hindering access to care. These data have informed future service planning at the Clinic.Ongoing data collection is key to maintaining a responsive, targeted service for a continually changing population.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".