Bibliographic record
Abstract
There is currently a worldwide shortage of registered nurses due in part to the profession being unable to retain nurses in a variety of specialty areas. The nurse shortage, however, is not a new phenomenon. A range of policies have been implemented throughout the last thirty years to increase the retention of this group, especially in the United Kingdom, the United States, Canada and Australia. A key policy in this regard has been the transfer of nurse education from hospitals to universities. In this paper we examine the effect of training on the employment behaviour of registered nurses in Australia. Using a unique dataset, this paper identifies the determinants of nurse retention within the health care sector in Australia's largest state, New South Wales, during the period from 1986 to 2002. A variety of duration models are estimated, using a 'single-spell and single-exit' hazard model, with and without unobserved heterogeneity. A variety of issues associated with the econometric analysis including censoring, appropriate methods for analyzing discrete survival time data, extensions of the basic hazard model, and data organizational issues are also discussed. Results from the duration models indicate that registered nurses trained at educational institutions in New South Wales are approximately 5.3 per cent more likely to leave the nursing profession, relative to hospital trained nurses. Other groups that exhibit a higher exit rate out of the nursing profession include male nurses, younger nurses, and those working in the private and community sectors, specialized in mental health, working in developmental disability services and in temporary employment. A number of possible reasons for the results identified in the empirical analysis are canvassed. It may be the case that the 'standardization' of nurse training, by converting it into a university degree, has increased the 'mobility of nurse qualifications' and helped to facilitate movement between the nursing market and other labour markets in NSW. In regard to policy implications, this paper does not assert that the shortage of registered nurses is best addressed by a move back to hospital-based training. Rather, the results serve to highlight the role played by training on subsequent labour market behaviour.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".