Bibliographic record
Abstract
Purpose The purpose of this paper is to outline the reflections of a person with lived experience of a severe mental illness (SMI) and former peer support worker in Montreal, Quebec, Canada, who has also worked for seven years in mental health research. It describes a tendency of resources and services to create ghettos of people with SMIs by failing to support the integration of people with SMIs into the community at large or in exploring options for meaningful, fulfilling occupation, reinforcing social exclusion and ghettoization. Design/methodology/approach This paper shows a reflective and narrative account of personal experiences and observations of the ghettoizing tendency in mental health services. Findings Mental healthcare professionals tend to support people with SMIs in engaging activities within resources for the mentally ill, and not in carrying out activities in the community at large. The range of activities offered is limited, an obstacle to finding meaningful, fulfilling occupation. Harmful psychological effects include self-stigma, low self-esteem and a sense of marginalization, generating a ghettoized mentality. The difficulties encountered in an effort to leave the mental health ghetto are touched on with examples of how to overcome them. Practical implications The need for professional support for social integration of people with SMIs is identified, which could ultimately favor social inclusion of people with SMIs. Originality/value It is written from the perspective of a user and provider of mental health services, who also has seven years’ experience in mental health research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.007 |
| 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".