Students learning in clinical practice, supervised in pairs of students – a phenomenological study
Bibliographic record
Abstract
Background: Clinical studies have an important position in Nursing Education, it is thus important to develop the learning strategies of students in order to facilitate their learning process during the clinical practice. The aim of the study is to describe the process of students’ learning towards their profession, when supported by supervision in pairs. Methods: Data has been collected through interviews of students during their clinical studies. The study has been carried out with a Reflective Lifeworld Research (RLR) approach founded on phenomenological traditions. The clinical settings are based on the model of the Developing and Learning Care Unit that has a structure that supports students in their learning towards becoming nurses. Results: Results show that structured supervision is favourable for students learning, where pair of students, space and time play a significant role. The results are illustrated in following themes: The significance of responsibility for learning, the strength and sensitivity in pairs of students, the focus on doing, the significance of the attitude of the supervisor, the vulnerability and potential of the learning environment and Reflection as a possibility and a pre-requisite. Conclusions: The study shows that the conduct of supervising in pair of students is of great importance for students’ learning and it is thus important to develop a reflective supervising approach and also knowledge of how to support students’ learning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".