Creating Inclusive Workplaces: Employing People with Psychiatric Disabilities in Evaluation and Research in Community Mental Health
Bibliographic record
Abstract
Abstract: People with psychiatric disabilities face barriers to employment both in the larger community and within the mental health system itself. Strategies used to affirmatively employ people with psychiatric disabilities as research personnel in an evaluation of community mental health services are described in this article. Our goal was to address two critical issues in the provision and evaluation of mental health services: the importance of meaningful work and productivity in the lives of people with psychiatric disabilities, and obtaining valid and reliable data regarding the effectiveness of community mental health services. A three-phase methodology for developing affirmative employment opportunities is presented, consisting of three components: affirmative planning, affirmative support, and affirmative rigour and method. The methodology is intended as a guide to assist evaluators and researchers in fulfilling the vision of the Canadian Human Rights Act and the Employment Equity Act.
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 | 0.243 | 0.217 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".