The quality of doctoral nursing education in South Africa
Bibliographic record
Abstract
BACKGROUND: The number of doctoral programmes in nursing has multiplied rapidly throughout the world. This has led to widespread concern about nursing doctoral education, specifically with regard to the quality of curricula and faculty, as well as to the availability of appropriate institutional resources. In South Africa, no study of these issues has been conducted at a national level. OBJECTIVE: To explore and describe the quality of nursing doctoral education in South Africa from the perspectives of deans, faculty, doctoral graduates and students. METHOD: A cross-sectional survey design was used. All deans (N = 15; n = 12), faculty (N = 50; n = 26), doctoral graduates (N = 43; n = 26) and students (N = 106; n = 63) at South African nursing schools that offer a nursing doctoral programme (N = 16; n = 15) were invited to participate. Data were collected by means of structured email-mediated Quality of Nursing Doctoral Education surveys. RESULTS: Overall, the graduate participants scored their programme quality most positively of all the groups and faculty scored it most negatively. All of the groups rated the quality of their doctoral programmes as good, but certain problems related to the quality of resources, students and faculty were identified. CONCLUSION: These evaluations, by the people directly involved in the programmes, demonstrated significant differences amongst the groups and thus provide valuable baseline data for building strategies to improve the quality of doctoral nursing education in South Africa.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".