Simulation in mental health interprofessional education
Bibliographic record
Abstract
Purpose: Mental health care involves multiple professionals from diverse backgrounds providing interdependent and complex services. Accordingly, care needs to be planned skillfully in partnership with health and social care providers and mental health service users. Working from an interprofessional lens enables professionals to work collaboratively to affect care that is safe and improves health outcomes. Yet, in practice there is often a disconnect between mental health care professionals that hinders collaborative practice and impacts the quality of care. Furthermore, stigmatization of mental illness continues to pervade health care professionals’ attitudes which serves to further compromise health outcomes. Interprofessional education (IPE) using simulation is proposed as an effective teaching and learning method to improve collaborative practice and decrease stigma amongst mental health care professionals in undergraduate education. Approach: Undergraduate nursing and pharmacy students from two universities participated in a one-day IPE event. During the event, students collaboratively interviewed standardized patients portraying mental health service users and developed an interprofessional plan of care. Faculty perspectives of the event were gathered to identify challenges and recommendations for ongoing implementation. Findings: Current literature and faculty facilitator feedback supports IPE using simulation as an effective teaching and learning strategy to develop therapeutic communication skills, address stigma amongst students prior to practice, clarify professional roles, and improve interprofessional collaboration. Faculty facilitator recommendations to improve the implementation of IPE with healthcare professionals during undergraduate education include early introduction of IPE, adequate preparation for students, realistic case scenarios, facilitator and standardized patient training, and funding to support events. Conclusion: The use of standardized patients in the context of interprofessional mental health education is a strategy with the potential to improve collaborative practice and address mental illness stigma amongst health care professionals. Further research with students is needed to evaluate the effectiveness of simulation in mental health IPE.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".