Law and Psychiatry Seminar: An Advanced Intervention in Interprofessional Education and Attitudinal Improvement
Bibliographic record
Abstract
Background: The tenuous relationship between psychiatrists and lawyers does not serve mental health patients in conflict with the law or society well. The characteristic miscommunication that occurs, though premised on differential pedagogical constructs, presents an opportunity to intervene from the pre-licensure educationalstage with the hope of positively affecting future practice.Methods: Law students and psychiatric residents were brought together to interact with each other and with instructors from the two fields through the Law and Psychiatry interprofessional seminar series. We examined the attitudes and perceived co-operation of learners in this seminar in comparison to a control group of law students (Human Rights class) who did not have any interprofessional interaction.Findings: Learners in the interprofessional seminar series reported more positive attitudes toward members of the other profession after completing the course. Additional positive changes in the level of perception of and actual co-operation with the other profession were noted with high satisfaction among participants.Conclusions: Learning activities that can foster positive interactions with and understanding of other professions may improve relations and collaboration in interprofessional education. The potential impact and benefit for the patient and the system are worthwhile.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".