Employment Interventions in Health Settings: A Systematic Review and Synthesis
Bibliographic record
Abstract
PURPOSE: Employment is a key social determinant of health. People who are unemployed typically have worse health than those employed. Illness and disability can result in unemployment and be a barrier to regaining employment. We combined a systematic review and knowledge synthesis to identify both studies of employment interventions in health care settings and common characteristics of successful interventions. METHODS: We searched the peer-reviewed literature (1995-2017), and titles and abstracts were screened for inclusion and exclusion criteria by 2 independent reviewers. We extracted data on the study setting, participants, intervention, methods, and findings. We also conducted a narrative synthesis and iteratively developed a conceptual model to inform future primary care interventions. RESULTS: Of 6,729 unique citations, 88 articles met our criteria. Most articles (89%) focused on people with mental illness. The majority of articles (74%) tested interventions that succeeded in helping participants gain employment. We identified 5 key features of successful interventions: (1) a multidisciplinary team that communicates regularly and collaborates, (2) a comprehensive package of services, (3) one-on-one and tailored components, (4) a holistic view of health and social needs, and (5) prospective engagement with employers. CONCLUSIONS: Our findings can inform new interventions that focus on employment as a social determinant of health. Although hiring a dedicated employment specialist may not be feasible for most primary care organizations, pathways using existing resources with links to external agencies can be created. As precarious work becomes more common, helping patients engage in safe and productive employment could improve health, access to health care, and well-being.
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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 | 0.033 | 0.117 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".