Factors Associated With Dropout, Retention and Graduation of Nursing Students in Selected Universities in South Africa: A Narrative Review
Why this work is in the frame
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Bibliographic record
Abstract
Much has been discussed in workshops, meetings, seminars and nursing summits in South Africa but very little has been revealed in literature on the scourge of drop out, retention and graduation rates of nursing students. The authors reviewed literature related to dropout, retention, completion and graduation rates of nursing students in selected universities in South Africa. Journal articles from 2007-2016 were reviewed for emerging themes about nursing students’ dropout, retention, completion, success and graduation. Exclusion criteria: online or distance education programmes, postgraduate programmes, experimental or randomized control trials and previous review studies. Comprehensive electronic search was conducted for published longitudinal and cross- sectional studies. Specific databases: PubMed, MEDLINE, EBSCO host, CINHAL. Specific search terms: [“student” OR “nursing”], OR [“dropout” OR, “retention”], OR [“graduation”, OR “education” OR “success” OR “completion”] AND “universities” OR “undergraduate” AND [“strategies” OR “interventions”]. Thirty- four (34) studies met review criteria. Fifteen (15) (47.06%) of the studies reported results on attrition, 16 (47.06%) reported on retention and 3 (8.82%) reported on completion and graduation. Academic, personal, preparedness and social factors were associated with dropout, retention and graduation of nursing students in South Africa. Dropout from undergraduate nursing programme is fraught with many problems. There is a need for retention models. Without nurses, much of the public health outcomes will be hardly achieved. If the problem of dropout and retention with decreased graduation persists, the health services will be crumbled thus affecting the realization of the health outcome “a long and healthy lifestyle for all”.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it