Health services for Aboriginal and Torres Strait Islander children in remote Australia: a scoping review
Bibliographic record
Abstract
Abstract In Australia, there is a significant gap between health outcomes in Indigenous and non-Indigenous children, which may relate to inequity in health service provision, particularly in remote areas. The aim was to conduct a scoping review to identify publications in the academic and grey literature and describe 1) Existing health services for Indigenous children in remote Australia and service use, 2) Workforce challenges in remote settings, 3) Characteristics of an effective health service, and 4) Models of care and solutions. Electronic databases of medical/health literature were searched (Jan 1990 to May 2021). Grey literature was identified through investigation of websites, including of local, state and national health departments. Identified papers (n=1775) were screened and duplicates removed. Information was extracted and summarised from 116 papers that met review inclusion criteria (70 from electronic medical databases and 45 from the grey literature). This review identified that existing services struggle to meet demand. Barriers to effective child health service delivery in remote Australia include availability of trained staff, limited services, and difficult access. Aboriginal and Community Controlled Health Organisations are effective and should receive increased support including increased training and remuneration for Aboriginal Health Workers. Continuous quality assessment of existing and future programs will improve quality; as will measures that reflect aboriginal ways of knowing and being, that go beyond traditional Key Performance Indicators. Gold standard models for service delivery have community leadership and collaboration. Increased resources with a focus on primary prevention and health promotion are essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".