Une simulation interprofessionnelle : une exploration des expériences des étudiantes en sciences infirmières
Bibliographic record
Abstract
BACKGROUND: the literature suggests that simulation is an effective strategy to meet the learning needs of nursing students. Traditionally, simulation learning for nursing students takes place at nursing schools ; at a distance from the clinical setting, patients, and the interprofessional team. AIM: the objective of this pilot project is to explore the experiences of Francophone nursing students following their participation in an interprofessional simulation in a hospital setting during their third year clinical placements. METHOD: a case study using Yin's (2003) approach was used to explore this phenomenon through focus groups and individual interviews. RESULTS: thirteen people participated in three simulation sessions that each included two scenarios. Content analysis of the focus groups revealed four themes : 1) the need for a realistic, but safe environment ; 2) simulation helps to build self-confidence ; 3) simulation improves knowledge of the role of the nurse ; and 4) simulation improves knowledge of teamwork. Two themes emerged from individual interviews : 1) the knowledge and skills acquired during the simulation were retained over time ; and 2) perceptions of the effects on the quality and safety of patient care. CONCLUSION: the use of simulation could be effective for the development of knowledge of nursing role, teamwork, and self-confidence.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".