<i>Partnering for Patti</i>: Shaping future healthcare teams through simulation-enhanced interprofessional education.
Bibliographic record
Abstract
BACKGROUND: is a sim-IPE experience collaboratively developed by faculty from Bachelor of Nursing and Respiratory Therapy programs within two Atlantic institutions leveled for third-year nursing and respiratory therapy students. This event provides an opportunity for participants to enhance their knowledge of the six CIHC IPE domains, and improve their understanding of and appreciation for IPC. Within this context learners must work together, and rely on the expertise of both professional groups to critically think through and improve a declining client scenario. Once complete, debriefing and reflective journaling help participants solidify learning and deduce new frames of understanding. It has been hypothesized that this event enhances student knowledge of CIHC IPE domains, and creates a deeper appreciation for, and understanding of IPC. The primary objective of this research was to determine if participants' understanding of CIHC IPE domains improved, and if perceptions of their own and the other profession were reframed as a result of this innovation. METHODS: This article describes the educators' approach in setting up and delivering this learning experience and the results of this event through students' perceptions. This cross-sectional study used a descriptive mixed-methods design. Two data collection tools were used to explore changes in participants' perceptions and event feedback. RESULTS: Data analysis found that the majority of participants identified value in this IPE learning experience. Qualitative and quantitative findings suggest participants developed a deepened appreciation for IPC and an improved understanding of the CIHC IPE competency domains. DISCUSSION: as a novel IPE learning experience. Although it is unclear to what degree objectives were met, findings strongly support continued integration of this learning experience.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".