Work participation for people with severe mental illnesses: An integrative review of factors impacting job tenure
Bibliographic record
Abstract
BACKGROUND: Enabling people with severe mental illness to sustain employment remains a challenge. This is despite most wishing to be employed, and the development of effective vocational interventions and employment supports for this population. To better understand how to enable their sustained involvement in the workforce, this review sought to identify, analyse and summarise studies investigating the factors that impact the job tenure of workers with severe mental illness, irrespective of the type of employment support they received. METHODS: An integrative literature review approach was employed to locate, appraise and synthesise quantitative and qualitative research focused on job tenure published in the 20 years up to 2013. Findings from nineteen studies were extracted and integrated using thematic analytic strategies. RESULTS: Job tenure was mostly conceptualised across the reviewed studies as time spent in individual jobs rather than as ongoing participation in the workforce. Three themes describe the factors contributing to job tenure: (1) the worker's experience of doing the current job; (2) natural supports in the workplace; and (3) strategies for integrating work, recovery and wellness, each of which could either support or impede ongoing employment. CONCLUSION: Occupational therapists, other vocational specialists and mental health staff can use these factors as a guide to supporting people with severe mental illness in employment. More detailed examination of job tenure is required in future research not only on job duration but also on the quality of jobs held, their value for career development and the role of services in supporting tenure.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".