Co-Constructive Patient Simulation at International Conferences: Strengthening Interactions and Deepening Reflection
Bibliographic record
Abstract
Abstract Clinical training in psychiatry can profit from methods that can be applied in different settings and circumstances, yet use a sound, scientifically proven concept that enhances the learning experience. One way to create a common international community of practice (ICoP) of child and adolescent psychiatrists (CAPs) is through participation in a patient simulation session at international conferences. A co-constructive patient simulation (CCPS) was conducted as a workshop at two international CAP conferences, AACAP/CACAP 2022 and ESCAP 2023, characterized by script co-construction, active learner involvement, and systematic debriefing intended to enhance reflective function in clinical practice. About 30 international learners participated each session. Two facilitators were from North America, two from Europe. The first session participants had to enroll in the workshop and the CCPS was played with professional actors. The second session registration was not required by the conference organization and the CCPS was played with volunteering actors with a background in psychology, unfamiliar to the public. Overarching themes included an appreciation of local and international differences in practice, legislation and clinical thinking, and shared challenges such as dealing with uncertainty, family dynamics, strong emotions, difficult behaviour and non-adjustable perspectives. This approach can provide or expand educational resources, reveal useful common ground, (cultural) differences, and important themes in clinical practice, facilitate reflective practice in real time, making international conferences more fun and interactive.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".