Work Experiences and Challenges to Employment Sustainability for People With Mental Illness in Supported Employment Programs
Bibliographic record
Abstract
Supported employment (SE) is an evidence-based program that has shown much promise in helping people with severe mental illnesses (SMIs) to gain and sustain competitive employment. However, there are significant variations in employment outcomes across SE programs in Canada that can be partly explained by SE service users’ experiences in their work environment. The work environment can exert a considerable influence on the interest in and ability to sustain the employment of a person with an SMI. This study explores the work experiences of individuals with SMIs who are involved in an SE program and who understand the challenges of and barriers to sustaining such employment. Semistructured, in-depth interviews were conducted with 15 individuals with SMIs, and the data were analyzed using a constructivist grounded theory approach. Challenges to the employment sustainability of an individual with SMI were found to arise primarily from three intersecting contexts: the SE program, the work environment, and the larger Canadian labor market. The findings suggest that SE programs will better promote employment sustainability if they adhere closely to individual placement and support model of SE. SE service providers (employment specialists) must be equipped with a wide range of knowledge and skills to meet the needs of individuals with SMIs if sustainable employment is to be achieved. It is recommended that there must be investment in training for employment specialists to assist SE service users to achieve sustainable employment outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".