Resilience and Associated Factors in Schizophrenia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Resilience in schizophrenia has been associated with multiple clinical variables that, to the best of our knowledge, do not include impulsiveness, aggression and also personality and insight with possible influences, which remain as poorly investigated topics. This study investigated the relationships of resilience with depression, aggression, impulsivity, personality and insight in order to assess the factors that explain resilience in schizophrenia. METHOD: The study included 139 individuals with clinically stable schizophrenia. Data were acquired by means of the Resilience Scale for Adults (RSA), the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS), the Schedule for Assessment of Insight (SAI), the Eysenck Personality Questionnaire Revised-Abbreviated (EPQR-A), the Barratt Impulsiveness Scale, 11th version (BIS-11) and the Buss-Perry Aggression Questionnaire (BPAQ). Correlations of the scores of the RSA with the scores of the other psychometric scales and the demographic and clinical data were evaluated. Linear regression analysis was used to determine the factors predicting resilience. RESULTS: The PANSS total and general psychopathology scores and scale scores on depression, impulsiveness and aggression were negatively correlated with resilience scores. Attentional impulsiveness, neuroticism and depression predicted low levels of resilience. There were no significant correlations between insight and the total or subdimension scores of resilience except for the subdimension structural style. CONCLUSION: Treatments focusing only on clinical remission in schizophrenia are not sufficiently effective. Interventions for enhancing resilience in schizophrenia should consider depressive symptoms, attentional impulsivity and personality traits such as neuroticism.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it