Bibliographic record
Abstract
The “Mindful of Machines: Mental Health AI, Rights, and the Role for Law” workshop, held at the University of Ottawa on February 23, 2024, brought together 36 interdisciplinary participants to examine whether Canada’s legal frameworks adequately ensure compassionate, rights-compliant mental health AI. The workshop included individuals with lived experience, healthcare professionals, AI researchers and developers, legal scholars, policymakers, and regulators, all focused on a central premise: strong legal frameworks must support service users’ dignity, equality, and autonomy. Participants heard expert presentations on key legal issues, engaged in vigorous discussion, and reflected on four questions: (1) What is AI’s greatest potential for mental health service users? (2) What are the biggest risks? (3) Is law adequate to support potential and guard against risks? And (4) what messages should we convey to law and policy makers? This report summarizes the workshop presentations, discussions, and findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.087 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".