Insight and subjective measures of quality of life in chronic schizophrenia
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".