Implementing Preceptorship in Baccalaureate Nursing Program in Middle-Low-Income Countries: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Preceptorship is critical for developing the next generation of nurses to provide high-quality patient care. However, challenges in implementing preceptorship programs in low-middle-income countries (LMICs) exist, affecting the quality of nursing care provided to patients. OBJECTIVES: To (1) explore the extent of literature on key strategies for effective implementation of undergraduate preceptorship nursing education in LMICs and (2) identify existing evidence and gaps in the literature about the implementation of preceptorship in LMICs. METHOD: This scoping review followed Arksey and O’Malley’s (2005) methodological framework. We used the following databases: CINHAL, PubMed, MEDLINE, and ERIC to conduct a systematic search of articles in 2023. The search strategies were focused on the following aspects: “preceptorship,” “baccalaureate nursing program,” “implementation strategies,” and “Low-middle-income countries.” Criteria for including the studies were a) preceptorship in undergraduate/ baccalaureate nursing programs, b) primary quantitative and qualitative studies, and c) implementation of preceptorships in LMICs. RESULT: Twenty-three (n=23) studies met the inclusion criteria. Five themes were identified from the analysis of the data: 1) Setting clear guidelines for preceptorship, 2) Preceptor professional development, 3) Strengthening preceptor roles in the development of future nursing force, 4) Preceptorship experience in clinical placement environment, and 5) Collaborative approach to preceptorship. CONCLUSION: This scoping review highlighted the importance of structured guidelines for preceptorship programs in elevating the quality of nursing education in LMICs. There is a paucity of evidence on preceptor professional development related to integrating evidence-based pedagogy in student supervision.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".