Changing the channel: the effect of an innovative video intervention on resident attitudes towards interprofessional collaboration on a Geriatric Medicine Unit
Bibliographic record
Abstract
Background: Medical learners develop a more positive attitude toward Interprofessional Collaboration (IPC) following Interprofessional Education (IPE) programs. However, IPE is not standardized, and the most effective teaching tool is unclear. The purpose of our study was to develop an IPE teaching tool for medical residents during an inpatient geriatric medicine rotation at an academic hospital, evaluate and explore the impact of the program on resident attitudes towards teamwork, and identify barriers and facilitators to interprofessional collaboration. Methods: An innovative video was developed which simulated a common IPC scenario. Near the start of the rotation, learners watched the video then participated in a facilitated discussion around principles of IPE, using the Canadian Interprofessional Health Collaborative (CIHC) framework, which highlights interprofessional communication, patient-centered care, role clarification, team functioning, collaborative leadership, and interprofessional conflict resolution. At the end of their four-week rotation, focus groups were conducted to explore resident attitudes towards IPE. The Theoretical Domain Framework (TDF) was used for qualitative analysis. Results: Data from 23 participants in five focus groups were analyzed using the TDF framework. Residents were able to identify barriers and facilitators to IPC in five TDF domains: environmental context and resources, social/professional role and identity, knowledge, social influences, and skills. Their observations correlated with the CIHC framework. Conclusion: The use of a scripted video and facilitated group discussion gave insights into residents' attitudes, perceived barriers, and facilitators towards IPC on a geriatric medicine unit. Future research could explore the use of this video intervention in other hospital services where team-based care is important.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".