Innovative education strategies implemented for large numbers of undergraduate nursing students: The Case of one South African university nursing department
Bibliographic record
Abstract
The nurse education and training landscape in South Africa has changed in different ways over the past century, with the result that education and training of nurses does not necessarily translate into an adequate supply of professional nurses for the health care service. Today there is a shortage in this category. Factors which contribute to this shortage include nurses moving from the public to the private sector due to perceived better conditions of service, migration, burden of disease, reduction in bed occupancy and an ageing nurse population. Many professional nurses are now reaching retirement, and it is imperative that the training and supply of young professional nurses for the country be reconsidered in the light of this. According to Pillay, the majority of nurses’ training begins in the public sector and their knowledge is grounded on this experience. When sufficient experience is gained, they seek out better opportunities in the private sector and migrate to the more developed countries. This loss of experience from the public sector impacts negatively on the capacity to mentor new graduates, which in turn results in the young, professional, trained nurses seeking better opportunities with organizations where they can develop further. The net result of this is that the public sector is left with overworked, older staff who are on the verge of retirement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".