Bibliographic record
Abstract
Background: The Norwegian government has indicated that health and socialstudies should emphasize interprofessional collaborative learning (IPL), especiallyin clinical placements. Through IPL, students have the opportunity to gain insightinto other professional responsibilities and minimize negative stereotypes. Thismight improve collaboration across professional boundaries. Professionals withcollaborative competence might solve complex health problems, and thus improvethe quality of healthcare. The objectives of this article are to investigate the IPLexperiences nursing students acquire through shadowing practice with differentprofessionals in home care.Methods and Findings: To develop a model for IPL, 12 nursing students spent five days shadowing four different healthcare professionals working in home care. At the end of the pedagogical intervention, the students reflected on the practice and the role of the different professionals they had followed. To investigate how the students experienced interprofessional shadowing practice, the reflective notes were analyzed, templates for the selected professionals were drawn up, and four focus group interviews were conducted The results showed that students has acquired knowledge of other professions’ responsibilities and were aware of thneed for an interprofessional approach to home care.Conclusions: This kind of shadowing might be an ideal model for educationalinstitutions seeking to implement IPL.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".