Mental Health and Legal Education
Bibliographic record
Abstract
The chapter "Mental Health and Legal Education" by Benjamin L. Berger and Lorne Sossin explores the critical intersection of mental health and legal education. It argues that mental health should be integrated into the law school curriculum, not just as an elective but as a fundamental aspect of learning across all legal domains. The authors highlight the need for law students to understand the implications of mental health on legal rights and obligations, emphasizing the importance of trauma-informed lawyering and the broader context of mental health in legal education. The chapter also addresses the mental health challenges faced by law students and legal professionals, advocating for a transformation in legal education to better support mental well-being. The authors call for a shift in how mental health is perceived and taught in law schools, suggesting that this change is essential for creating a more just and sensitive legal system. For those interested in a deeper exploration of these themes, readers are encouraged to reach out to the authors for the full chapter. Reproduced with permission. Copyright 2024 LexisNexis Canada. All rights reserved. The information provided herein is for general informational purposes only and is not, nor should it be construed as, legal advice. Reproduction of this material, in any form, is specifically prohibited without written consent from LexisNexis Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".