Factors Associated With Dropout, Retention and Graduation of Nursing Students in Selected Universities in South Africa: A Narrative Review
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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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".