BOS3b.003 Utilizing simulated learning to develop non-clinical skills: a unique approach to improving advance care planning processes
Bibliographic record
Abstract
Background Simulation learning is often associated with clinical skills development, particularly for patients facing high risk urgent situations. In these learning environments, clinicians typically practice urgent acute skills such as Cardio Pulmonary Resuscitation, intubation, and chest tube insertion. Recognizing how successful this learning method is for tangible skills, Fraser Health’s Regional Advance Care Planning (ACP) Team proposed this unique and innovative learning approach for use with other essential skills, namely communication. The teams’ objectives were: Increase familiarity with advance care planning processes, Facilitate knowledge translation of topics such as medical order designation, symptom management, and end of life care. Improve communication skills between interdisciplinary health care providers, patients and the people who matter most to them. Establish a debrief culture for reflection and learning. Demonstrate a shared decision making model. Methods Quality Improvement Results In 2022, pilot funding for the project was secured from the Physician Facilitated Engagement Program. An interdisciplinary panel of health care providers created two case studies. Following this, four interdisciplinary acute care simulation sessions were held at a community hospital. The sessions concluded with an extensive debriefing session and post-participation survey to assess confidence and provide suggestions for improvement. Conclusion In this oral presentation, details of the cases, debriefings and surveys will be shared and be the primary teaching tool. Participants will be encouraged to explore implementing this unique non clinical simulation learning in their own settings of care. Learners will be able to apply simulation learning approach to improve confidence of HCPs to engage in Advance Care Planning, Serious Illness, and End of Life conversations and processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".