Nursing students’ learning to involve elderly patients in clinical decision making – The student perspective
Bibliographic record
Abstract
The increasing number of elderly people in the population triggers a need for more nurses in the eldercare services. Therefore, a need exists to encourage nursing students’ interest in eldercare. International research found both positive and negative attitudes towards eldercare. The challenge is to facilitate students’ learning about and interest in geriatric care. This study aimed to investigate whether listening to older patients’ narratives may facilitate nursing students’ competencies related to and their interest in eldercare. A phenomenological-hermeneutic approach was employed to investigate whether an intervention in which nursing students conduct narrative interviews with older patients may promote their competencies to involve these patients in their own care while concurrently enhancing their interest in eldercare. New knowledge was generated through the interpretation of transcribed narrative interviews with the students conducted before and after the intervention. Four themes emerged: the significance of the narrative for the patient-nurse relation, for involving patients in clinical decision making, for person-centred care and for students’ interest in eldercare. The students valued the impact of the narrative interview. After the interview, they experienced a better patient-nurse relation and they found that it was easier to involve elderly patients in clinical decisions and to provide person-centred care. Students expressed a more positive interest in eldercare. This research addresses geriatric care, as it conveys experiences with the use of narratives to facilitate students' learning about eldercare.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".