From margins to mainstream: What do we know about work integration for persons with brain injury, mental illness and intellectual disability?
Bibliographic record
Abstract
Employment is a right of citizenship and a social determinant of health, but employment rates remain low for persons with disabilities. The purpose of this paper is to examine the principles and practices guiding work integration within the fields of intellectual disability (ID), brain injury, and mental illness and to identify best practices to support transitions to employment across these three groups. This integrative review drew upon an occupational perspective to analyze the current literature. Findings reveal that the need and benefits of working are recognized across disability groups but that philosophical perspectives guiding work integration differ. In the area of mental illness, recovery is seen as a process within which work plays an important role, in ID work is viewed as a planned outcome that is part of the developmental process, and in the field of brain injury, outcomes of employability and employment are emphasized. A common theme across the three disability groups is that in order to facilitate work integration, the person, the job and the work environment are important factors in need of examination. Evidence pointing to the effectiveness of the supported employment model is increasing across these three populations. A framework for guiding the development of further research and for promoting changes to support work integration is presented.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".