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Determinants of quality of life in first‐episode psychosis

2003· article· en· W2071507227 on OpenAlexaff
Ashok Malla, Ross Norman, Terry McLean, C. MacDonald, Elizabeth McIntosh, F. Dean‐Lashley, Vincent J. Lynch, Derek Scholten, Rania S. Ahmed

Bibliographic record

VenueActa Psychiatrica Scandinavica · 2003
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of WaterlooWestern UniversityMcGill UniversityDouglas Mental Health University InstituteDouglas College
Fundersnot available
KeywordsPsychosisQuality of life (healthcare)PsychologyPsychiatryMedicineGerontologyClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess patient and/or illness characteristics associated with aspects of quality of life (QOL) in first-episode psychosis (FEP). METHOD: Patient characteristics, symptom ratings and Wisconsin QOL scale (client version) were assessed. Data were analysed with correlation coefficients and a hierarchical regression analysis. RESULTS: Patients presented with varying levels of QOL on different domains. The level of 'general satisfaction' was related to age of onset and social premorbid adjustment; 'weighted index of QOL' to social premorbid adjustment and inversely to educational premorbid adjustment; 'social relations' inversely to duration of untreated psychosis (DUP), length of prodrome and negative symptoms; 'psychological well-being' inversely to depression and educational premorbid adjustment; 'activities of daily living' to social premorbid adjustment and inversely to negative symptoms; and 'outlook on symptoms' to level of depression. CONCLUSION: Domains of self-rated QOL in FEP patients are differentially associated with malleable and non-malleable aspects of patient and illness characteristics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.351
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), 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

Citations63
Published2003
Admission routes1
Has abstractyes

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