ELSiTO. A Collaborative European Initiative to Foster Social Inclusion with Persons Experiencing Mental Illness
Bibliographic record
Abstract
ELSiTO (Empowering Learning for Social Inclusion Through Occupation), an international collaborative partnership, with over 30 members from Belgium, Greece and the Netherlands, aimed to explore the nature and processes of social inclusion for persons experiencing mental illness. Members included persons experiencing mental illness and health professionals. Four international visits and local activities enabled a knowledge creation process that combined the experience of social inclusion as we lived and worked together with exploration of the processes of community projects, narratives of experiences and reflective workshops. Outcomes included identification and description of the following: the nature of social inclusion as lived by the participants; the critical elements that support inclusion; and the competences that are developed by all stakeholders during this process. The complex process of social inclusion is facilitated by doing together in environments that enable equality, trust, risk taking and realignment of power. These conceptual understandings of inclusion are discussed in relation to the ongoing activities of the partners, to their potential contribution to the education of occupational therapists (and other professionals) and to the development of socially inclusive occupation-based projects in the community.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.001 |
| 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".