Clinical experiences of RN to BScN nursing students in Kenyan universities
Bibliographic record
Abstract
Introduction and objective: Clinical learning environments play a great role in nursing training as they allow nursing students to develop their clinical skills by combining cognitive, psychomotor and affective skills. Consequently, clinical learning environments enable nursing students to bridge the theory-practice gap. Fewer studies have examined the clinical experiences of RN to BScN students in Kenya. This paper is part of analytical memo of a larger PhD study that sought to explore and describe the support needs of RN to BScN students in Kenyan universities. The paper focuses on clinical experiences of RN to BScN students.Methods: Using a qualitative phenomenological approach, ten focus group discussions were conducted with 100 RN to BScN students, purposively sampled from four universities in Kenya. Data were analyzed using Tesch’s data analysis protocol. The article has adhered to Consolidated criteria for reporting qualitative studies.Results: The data on RN to BScN students’ clinical experience revealed two themes: curriculum challenges and practice environment and six sub-themes: redundant learning outcomes, redundant clinical assessments, not acknowledging prior learning, lack of clinical supervision, lack of learning resources and “an extra pair of hands”.Conclusions: The study findings highlight the need for review of clinical learning outcomes for the RN to BScN students in Kenya. The findings emphasize the need for collaborative partnerships between universities, clinical learning environments, nurse educators, and policy makers, to design of clinical learning outcomes relevant to RN to BScN students in Kenya.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".