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Meaningful lives: supporting young people with psychosis in education, training and employment: an international consensus statement

2010· article· en· W3115792767 on OpenAlexaboutno aff

Bibliographic record

VenueEarly Intervention in Psychiatry · 2010
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentVocational educationSupported employmentPsychosisAutonomyPsychologyPsychiatryStatement (logic)MedicinePolitical scienceEconomic growthPedagogyWork (physics)EconomicsLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.349
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2010
Admission routes1
Has abstractyes

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