Nursing workforce in very remote Australia, characteristics and key issues
Bibliographic record
Abstract
OBJECTIVE: To describe the nursing workforce in very remote Australia, characteristics and key issues. METHODS: Data were collected from four main sources: the refined CRANAplus database of remote health facilities; the 2006 census which provided population and percentage of Indigenous people in communities in very remote Australia; a national survey on occupational stress among nurses and an earlier study into violence and remote area nurses conducted in 1995. A descriptive analysis of the data was conducted. SETTING: Health facilities in very remote Australia. RESULTS: The registered nursing workforce in very remote Australia is mostly female (89%) and ageing, with 40.2% 50 years or over, compared to 33% nationally. Many (43%) are in remote Indigenous communities. Over the last decade, there has been a significant decrease in registered nurses with midwifery qualifications (55%) and in child health nurses (39%) in very remote Australia. Only 5% have postgraduate qualifications in remote health practice. CONCLUSION: The nursing workforce in very remote areas of Australia is in trouble. The workforce is ageing, the numbers of nurses per population has fallen and the numbers of midwives and child health nurses have dropped significantly over the last 15 years. As many of these nurses work in Indigenous communities, if these trends continue it is likely to have a negative effect on 'closing the gap' in Indigenous health outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".