“Seeing the patient as a human is their priority” – Patients’ experiences of being cared for by pairs of student nurses
Bibliographic record
Abstract
Background: A Developing and Learning Care Unit (DLCU) is a model used in the clinical practice of student nurses that aims at bridging the gap between theory and praxis, by supporting nursing students’ learning through supervision in pairs. The aim of this study is to describe how patients experience being cared for by pairs of student nurses.Methods: The study is based on a reflective lifeworld research (RLR) approach founded on phenomenological traditions. Data was collected in lifeworld interviews of 17 patients cared for by pairs of student nurses. The data was explored and analysed for meaning.Results: To be cared for by student nurses, supervised in pairs entails being involved in the students’ learning and being met with responsibility and a willingness to care and learn. This means being made the centre of attention, being seen, taken seriously and being listened to as a valuable human being. The students’ care is shown to be more flexible and has a more open approach, in comparison to that of the ordinary staff, and they ‘do something extraordinary’ and give of their time.Conclusions: Pairs of students, who are supervised within a learning model that support students’ learning through reflection, can contribute to patient experiences of being given good care.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".