Using interprofessional simulation to improve collaborative competences for nursing, physiotherapy, and respiratory therapy students
Bibliographic record
Abstract
Within the care of people living with respiratory conditions, nursing, physiotherapy, and respiratory therapy healthcare professionals routinely work in interprofessional teams. To help students prepare for their future professional roles, there is a need for them to be involved in interprofessional education. The purpose of this project was to compare two different methods of patient simulation in improving interprofessional competencies for students in nursing, physiotherapy, and respiratory therapy programmes. The Canadian Interprofessional Health Collaborative competencies of communication, collaboration, conflict resolution patient/family-centred care, roles and responsibilities, and team functioning were measured. Using a quasi-experimental pre-post intervention approach two different interprofessional workshops were compared: the combination of standardised and simulated patients, and exclusively standardised patients. Students from nursing, physiotherapy, and respiratory therapy programmes worked together in these simulation-based activities to plan and implement care for a patient with a respiratory condition. Key results were that participants in both years improved in their self-reported interprofessional competencies as measured by the Interprofessional Collaborative Competencies Attainment Survey (ICCAS). Participants indicated that they found their interprofessional teams did well with communication and collaboration. But the participants felt they could have better involved the patients and their family members in the patient's care. Regardless of method of patient simulation used, mannequin or standardised patients, students found the experience beneficial and appreciated the opportunity to better understand the roles of other healthcare professionals in working together to help patients living with respiratory conditions.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".