Barriers and facilitators of the effectiveness of the clinical pedagogical supervision of nursing and obstetric students in sub-Saharan Africa: A systematic review
Bibliographic record
Abstract
Objective: Education in nursing and obstetrics combines theoretical learning with clinical experience. Thus, the internship organization and the supervision of a trainee require the cooperation of different partners in educational and health institutions that receive the trainees. This systematic review aimed to identify factors that facilitate or hinder the effectiveness of clinical educational supervision of nursing and obstetrics students.Methods: Three electronic databases (PubMeb, CINAHL and ERIC) were searched. Two independent reviewers selected eligible publications based on inclusion and exclusion criteria. Qualitative, quantitative or mixed studies conducted in Sub-Saharan Africa and published between January 2011 and December 2020 were included.Results: The study revealed that while there are some strengths (facilitators), clinical pedagogical supervision presents mostly weaknesses (barriers) at the structural and procedural levels. Of the 65 factors studied, all nine studies were unanimous that 54 were barriers and 3 were facilitators. In addition, eight factors were cited as both barriers and facilitators.Conclusions: Clinical pedagogical supervision of nursing and midwifery students in sub-Saharan Africa faces major challenges of diverse origins that may undermine its effectiveness. It would be appropriate at the country level to analyze the barriers inherent in this supervision's structure and process and improve them.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".