A Comprehensive Framework to Advance Equity, Diversity, and Inclusion in a Forensic Service.
Bibliographic record
Abstract
Minority and Indigenous populations have disproportionate representation within forensic mental health services. Social determinants of health and systemic discrimination have contributed to the difficulties these populations have in accessing care, as well as significant differences in care trajectories. In addition, staffing and structural equity, diversity, and inclusion (EDI) challenges permeate forensic systems as in other health care settings. There is little literature to guide forensic mental health services in how best to provide equitable, diverse, and inclusive practices for patients, families, and staff. The forensic service at a major urban center in the Canadian province of Ontario has adapted an EDI framework to describe the processes employed to organize and integrate EDI principles and initiatives within a culture of learning and continuous improvement. This Forensic EDI Framework is composed of six domains: Organizational Commitment, Staff/Workforce Competencies, Service Access and Delivery, Promoting Responsiveness, Community Outreach, and Data Collection. Initiatives within each of these domains form the foundation of a sustainable platform for forensic service EDI practices that will promote lasting change.
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 | 0.056 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.011 | 0.009 |
| 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".