"Doing" social inclusion with ELSiTO: Empowering learning for social inclusion through occupation
Bibliographic record
Abstract
OBJECTIVES: The European partnership ELSiTO aimed to develop understanding of the nature and processes of social inclusion for persons experiencing mental illness. PARTICIPANTS: Partners were from Belgium, Greece and The Netherlands with over 30 members including mental health service users, occupational therapists and other staff. APPROACH: A knowledge-creation learning process was used during four international, experiential, visits and local meetings, which included visiting and describing good practice, telling stories of experiences, reflection and discussion. RESULTS: The partnership developed understandings of the nature and process of social inclusion, including both subjective and objective aspects interrelated with the doing of daily activities in the community. Members' work-related experiences, illustrated through their stories, depict the subjective aspects of social inclusion as they are shaped and framed by the objective conditions within a variety of work opportunities. Experiences in paid work, supported employment and voluntary work may both threaten and enhance mental health. Features of successful (voluntary) work experiences are identified. CONCLUSIONS: The importance is revealed of looking critically at current understandings of work and to move beyond a narrow focus on paid work in order to provide a range of work opportunities that will empower the individual's potential and promote inclusive communities.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".