A Case Report of the Conversion of Sheltered Employment to Evidence-Based Supported Employment in Canada
Bibliographic record
Abstract
This case report describes the transformation of a sheltered workshop program to a program that provides evidence-based supported employment services in partnership with five community treatment teams. Over a 15-year period, a Canadian nonprofit agency that provides employment services for persons with severe mental illness made a series of programmatic changes to increase the effectiveness of the services. The agency initially modified its facility-based sheltered workshop to include a prevocationally oriented work preparation program, later added brokered supported employment services, and finally completely transformed its organization by relocating its vocational rehabilitation counselors to five community mental health teams, in order to implement an evidence-based supported employment program that is based on the individual placement and support model. During the initial period in which the sheltered employment program was utilized, less than 5 percent of clients who were unemployed when they entered the workshop achieved competitive employment annually. The annual competitive employment rate did not increase during the prevocational phase; it increased during the brokered supported employment phase but did not exceed 25 percent. By contrast, after shifting to evidence-based supported employment, 84 (50 percent) of 168 unemployed clients who received between six and 27 months of individual placement and support services achieved competitive employment. This article also documents the role of agency planning and commitment quality improvement in implementing 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".