Preceptorship: Exploring the experiences of final year student nurses in acute hospital setting.
Bibliographic record
Abstract
Background: Preceptors play a pivotal role in inducting, supporting, teaching and assessing students on clinical placement. This research sought to examine student nurses’ experiences of preceptorship during their clinical placement in their final year of studies in order to further illuminate what is known about preceptorship in Ireland. Method: A qualitative research design was adopted for this study. Forty-seven final year nursing students were questioned using a structured enquiry schedule about their experiences of preceptorship during clinical placement. All participants were female. The data were analysed thematically according to Smith, Flowers and Larkin’s (2009) framework. Results: The results indicate that while a small minority found the experience of preceptors enhanced their learning while on clinical placement, the majority has a less than optimal experience. Reasons for this included: busy workloads of preceptors, difficulty in the accessibility of the preceptor and lack of preceptor training. Conclusions: The results highlight a number of challenges facing students and preceptors in the study. The authors advocate for a more systematic national study into preceptorship implementation in Ireland. This is necessary in order to inform a more coherent framework with national standards for preceptor training and implementation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".