Responding to Post-School Education Policy Reforms: A Case Study on the Incorporation of Nursing Colleges into the Post-School Education and Training System of South Africa
Bibliographic record
Abstract
Amongst the diverse providers of nursing education in South Africa, public nursing colleges have over the years produced 80% of pipeline nursing professionals. The demand imposed by the reorganisation of health services toward universal health coverage, together with the recent changes to the post-school legislation introduced by the Department of Higher Education and Training has required a repositioning of nursing colleges within the new milieu. If public nursing colleges did not comply with post-school education prescripts, they would not be eligible to offer programmes that are aligned to the Higher Education Qualification Sub-framework. The purpose of this article is to provide an account on progress and lessons learnt towards repositioning public nursing colleges within the new higher education milieu as a legal requirement for offering new nursing programmes leading to registration in any of the new nursing categories prescribed in the Nursing Act. The National Department of Health has, through the stewardship of its Chief Nursing Officer facilitated an intense process from 2016 to 2019 of preparing public nursing colleges to meet the requirements for accreditation as higher education institutions. Chief among these activities was the development of a national policy for nursing education informed by and designed around health service demands and underpinned by higher education principles to direct provisioning of nursing education and training. Parallel to the policy, the state of readiness of public nursing colleges was measured against the Council for Higher Education determined criteria for programme accreditation. Lessons emanating from this process are being used to accelerate preparation for accreditation of programmes leading to professional qualifications in nursing and other related health sciences programmes offered at college level to ensure sustained production of nurses with requisite skills mix required for a responsive health care system.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".