Nursing students’ learning experiences in clinical placements or simulation–A qualitative study
Bibliographic record
Abstract
This paper reports on a qualitative study whose aim was to investigate nursing students' learning experiences in two arenas. It is common practice all first-year nursing students to practise in a skills lab. In this study, students practised in either clinical settings or a skills lab. In the design, a phenomenological-hermeneutic approach was used. The setting was Course 2, a ten-week course including either two weeks on clinical placements or two weeks in a skills lab. The participants were six first-year students. Data were generated by participant observations and interviews and were interpreted according to Ricoeur’s theory of interpretation. The findings indicated that students learned nursing skills in both arenas. However, on clinical placements, students and preceptors began nursing the patients after 20 minutes and students subsequently reflected on practice. In the skills lab, preceptors guided the students for up to an hour before they were ready to begin performing nursing. Students with previous nursing experience and activist learning style preferred to learn on clinical placements. Students with other learning styles – even one student with previous nursing experience – seemed to prefer learning in the lab, where they felt safe, as there was no risk of harm to patients. The conclusion was that, rather than all first-year students practising in the lab, it could be valuable to consider the students’ prior experience and preferred learning style in discussions of where to begin the learning trajectory in the nursing programme.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".