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Record W2096803198 · doi:10.1016/j.scog.2015.05.002

Insight and subjective measures of quality of life in chronic schizophrenia

2015· article· en· W2096803198 on OpenAlexafffund
Cynthia Siu, Philip D. Harvey, Ofer Agid, Mary Miu Yee Waye, Carla Brambilla, Wing‐Kit Choi, Gary Remington

Bibliographic record

VenueSchizophrenia Research Cognition · 2015
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersGenentechNeurocrine BiosciencesNational Institute of Mental HealthH. Lundbeck A/SMedicure
KeywordsPsychologySchizophrenia (object-oriented programming)Quality of life (healthcare)Quality (philosophy)Clinical psychologyCognitive psychologyApplied psychologyPsychiatryPsychotherapistEpistemology

Abstract

fetched live from OpenAlex

Lack of insight is a well-established phenomenon in schizophrenia, and has been associated with reduced rater-assessed functional performance but increased self-reported well-being in previous studies. The objective of this study was to examine factors that might influence insight (as assessed by the Insight and Treatment Attitudes Questionnaire [ITAQ] or PANSS item G12) and subjective quality-of-life (as assessed by Lehman QoL Interview [LQOLI]), using the large National Institute of Mental Health Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) dataset. Uncooperativeness was assessed by PANSS item G8 ("Uncooperativeness"). In the analysis, we found significant moderating effects for insight on the relationships of subjective life satisfaction assessment to symptom severity (as assessed by CGI-S score), objective everyday functioning (as assessed by rater-administered Heinrichs-Carpenter Quality of Life scale), clinically rated uncooperativeness (as assessed by PANSS G8), and discontinuation of treatment for all causes (all P > 0.05 for statistical interaction between insight and subject QoL). Patients with chronic schizophrenia who reported being "pleased" or "delighted" on LQOLI were found to have significantly lower neurocognitive reasoning performance and poorer insight (ITAQ total score). Our findings underscore the importance of reducing cognitive and insight impairments for both treatment compliance and improved functional outcomes.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.177
GPT teacher head0.399
Teacher spread0.222 · 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

Citations61
Published2015
Admission routes2
Has abstractyes

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