Retaining a viable workforce: a critical challenge for nursing
Bibliographic record
Abstract
Nursing is facing a crisis nationally and internationall, with Australia, the United States, New Zealand, Canada, the United Kingdom and Western Europe experiencing critical shortages of nurses. Problems with recruitment, retention and an ageing workforce means that attempts to ensure a viable nursing workforce must be placed at the top of the professional agenda. Strategies currently used to manage the crisis, such as overseas recruitment, are not sustainable and are ethically dubious. The demographic timebomb is ticking and up to half the current nursing workforce will reach retirement age by 2020. It is vital that there are adequate numbers of skilled and qualified nurses to take their places. Nursing and nurses are facing unprecedented challenges and pressures in the workplace. Job satisfaction is threatened as nurses are pressured to do more with less, Nursing productivity has increased phenomenally over the past ten years in response to increased demands and decreasing numbers of staff. The nursing workplace has disturbingly high levels of occupational violence, and many nurses operate within a culture of blame and scapegoating. There is evidence that organizational change is imposed upon nurses with little or no consultation and the literature reveals that this has a direct and negative effect on job satisfaction and on retention of nurses. This paper explores some of the critical issues that nursing must confront to be successful in establishing and maintaining a vigorous, dynamic and viable workforce.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".