Choosing a nursing career: Building an indigenous nursing workforce
Bibliographic record
Abstract
Introduction: This paper provides an overview of the impact of government policy in supporting the growth of an Indigenous nursing workforce in New South Wales and Australia.Methods: Publically available nursing workforce performance reports along with current literature were reviewed to provide a perspective on the current situation.Results and discussion: The National partnership agreement on closing the gap in Indigenous health outcomes indicated that to improve Indigenous health outcomes, significant investment is required with particular reference to increasing an Indigenous workforce. As nurses comprise the majority of the health workforce a number of strategies and initiatives have been put in place to support this agreement. Even though there has been commitment through government policy and funding initiatives it is questionable if this is having any real impact on growing an Indigenous nursing workforce.Conclusions: Recruitment strategies that will increase the number of Indigenous nurses within the health workforce requires a multilevel approach. Despite efforts to increase Indigenous nursing workforce numbers, there has been limited impact to any real successful gains since 2008. Building and growing an Indigenous nursing workforce that will support the “Closing the Gap” initiative will require significant continuing effort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".