Social firms: A means for building employment skills and community integration
Bibliographic record
Abstract
BACKGROUND: Social firms are widely used in Europe as a means of affirmatively creating employment opportunities and training for employment challenged groups. These commercial businesses produce, market and sell goods and services to the public while providing opportunities for productive engagement, increased incomes, and social integration for their employees. METHODS: This article presents a case study of a Norwegian social firm that was created to improve employment and functional outcomes for workers with mental health disabilities and addictions. The case illustrates one model of social firm, and is used as the foundation for discussion of the relative contributions of social firms to employment outcomes for people who are marginalized in the labour market. RESULTS: The social firm represented a major change in philosophy and operations for mental health service provision in the local municipality. Numbers of individuals served increased dramatically, and changes were observed in the extent and nature of participant daily involvement, and in outcomes achieved. This model brings participants into contact with the public, and has served to break down barriers and reduce stigma. CONCLUSIONS: Social firms represent a viable alternative for creating employment options and training and for enhancing social integration of people with mental health disabilities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".