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Social functioning in early psychosis: are all the domains predicted by the same variables?

2012· article· en· W1910512448 on OpenAlexafffundabout
Geneviève Bourdeau, Marjolaine Massé, Tania Lecomte

Bibliographic record

VenueEarly Intervention in Psychiatry · 2012
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsPsychologyPsychosisClinical psychologyBeck Depression InventoryDepression (economics)Social skillsIntervention (counseling)Independent livingRating scaleSocial functioningPsychiatryDevelopmental psychologyGerontologyMedicineAnxiety

Abstract

fetched live from OpenAlex

AIM: The study aims to determine the predictive value of negative symptoms, depression, short-term verbal learning and gender on three areas of social functioning--social life, vocational functioning and independent living skills--in a sample of 88 individuals with early psychosis. METHODS: Participants were recruited from early psychosis intervention programmes and community mental health clinics in British Columbia, Canada, and completed the following measures: client's assessment of strengths, interests, and goals, brief psychiatric rating scale, Beck depression inventory and California verbal learning task. RESULTS: Multiple linear regressions revealed that: more negative symptoms and higher depression predicted a less active social life; more negative symptoms and poorer short-term verbal learning ability predicted lower vocational functioning; and more negative symptoms and male gender predicted lower independent living skills. CONCLUSION: Results suggest that negative symptoms are predictive of all three areas of functioning but that specific variables add significant unique variance to individual areas of social functioning. Although a global social functioning score can be considered useful, greater precision can be gained by the use of domain-specific measures.

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)0.001
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 score0.029
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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

Citations25
Published2012
Admission routes3
Has abstractyes

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