Meaningful lives: supporting young people with psychosis in education, training and employment: an international consensus statement
Bibliographic record
Abstract
AIM: Unemployment is the major disability faced by people with psychotic illness. Unemployment rates of 75–95% are found among those with schizophrenia. Unemployment is associated with poorer social and economic inclusion, greater symptomatology, decreased autonomy and generally poorer life functioning. Unemployment also makes up over half of the total costs associated with psychotic illness. METHODS: A meeting was convened in London in June 2008. Invitees to this meeting included people from the USA, Canada and the UK interested in vocational intervention in early psychosis from either a research, clinical, economic or policy point of view. From this meeting a larger group–the International First Episode Vocational Recovery (iFEVR) group–has developed an international consensus statement about vocational recovery in first episode psychosis. RESULTS: The document is a basic statement of the rights of young people with psychosis to pursue employment, education and training; the evidence which exists to help them do this; and ways in which individuals, organizations and governments can assist the attainment of these ends. CONCLUSION: It is hoped that the Meaningful Lives consensus statement will increase the focus on the area of functional recovery and lift it to be seen in parallel with symptomatic recovery in the approach to treating early psychosis.
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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 | 0.078 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".