Nursing Students Perceptions on the Use of Preceptors to Improve Clinical Competence at the University of Namibia
Bibliographic record
Abstract
The purpose of this study was to investigate the perceptions of nursing students in relation to the use of preceptors to improve their clinical competence at the School of Nursing at the University of Namibia. Thus, the objectives of the study were to assess and describe the perceptions of nursing students regarding the use of preceptors to improve their clinical competence with a view to make recommendations based on the findings of the study. A quantitative, descriptive, cross-sectional design was used with a total of 100 nursing students from all four cohorts for the Bachelor of Nursing (Honours) (Clinical) at one of the university campuses being conveniently to participate in the study. Self-administered questionnaire were used to collect the data from the participants. The data derived from the questionnaire was analysed using SPSS version 24. The findings from the study revealed, inter alia, that 70% of the participants indicated that the use of preceptors has a positive effect on the students’ clinical competence. The study therefore recommended sustainment and strengthening of preceptorship strategies for clinical accompaniment of students. Notwithstanding positive findings, the study also recommends improvement of skills for teaching and time management skill, punctual report on duty and efficient utilization of clinical time for preceptors for more efficient implementation of student teaching during clinical accompaniment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".