Nursing students gain tools for knowledge utilisation through a work and research integrated learning assignment – a qualitative study
Bibliographic record
Abstract
Background: By integrating education, research and collaboration with society, in a learning assignment, the level of nursing students learning can be enhanced. Aim: The purpose of this study was to explore nursing students’ experiences of participating in a work and research integrated learning assignment during their clinical practice courses. Methods: At the end of their nursing training, in their sixth semester, five nursing students were recruited to the study and interviewed after which the texts of interviews were analysed using content analysis. The participants had been involved in two studies, one during their second semester, where the risk of falling was assessed, and one during their sixth semester where the risk for under nutrition was assessed. Results: The students experienced that the two assignments enhanced their learning in how to work as a foreman/supervisor, how to inform and engage in dialogue, about using risk assessment, and further, gave them the opportunity to meet the people behind the diagnosis. Through assignments they could also identify the need for knowledge within the study focus, for instance, the risks for falling or the risk of undernourishment. Further, they described how they tried to live up to the ethical standards and that they had learned about using a scientific approach in their work. Conclusion: It is possible to integrate Research, Education and Collaboration in a learning assignment during Nursing education (RECN-assignment) by allowing nursing students to participate in an actual research project during their work integrated learning courses. Such an approach enhances nursing students learning about research and the area being studied in the actual research project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| 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".