Shaping an Australian nursing and midwifery specialty framework for workforce regulation: criteria development
Bibliographic record
Abstract
One of the biggest obstacles identified in achieving Millennium Development Goals (MDGs) was the lack of available qualified health personal to meet the health needs of the global population. With nurses being the main workforce component in health systems, the human resource challenge for most countries is to address the reported shortage of nurses. Skill mix is one suggestion. In Australia, workforce projections indicated a shortage of 40,000 nurses by 2010. Toward the reform of the Australian health workforce, one project aimed to develop a nationally consistent framework for nursing and midwifery specialization based on knowledge and skills to generate the first national database iteration for designated specialties. A literature review looked at the way nursing specialty practices were defined in the United Kingdom, the United States of America and Canada. Three international and three national sources of criteria for specialty nursing practice were mapped against each other. The result was six criteria synthesized to define nursing practice groups as Australian nursing specialties. Each criterion was operationalized with criteria indicators to meet Australian expectations. The nurses in Australia commented on the criteria before they were finalized. An audit of national workforce databases identified nursing practice groups. The criteria were applied to identify nursing specialties and practice strands that would form a national nursing framework. This paper reports on the criteria developed to assess specialty practice at a national level in Australia.
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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 | 0.175 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".