Povezanost samostigme i uvida u bolest s depresivnošću i suicidalnošću u oboljelih od shizofrenije
Bibliographic record
Abstract
The aim of the study was to estimate relationship of insight into the disease and \ninternalized selfstigma on depression and suicidality. The study was conducted using \ncross-sectional method among 149 patients diagnosed with schizophrenia who were \ntreated at the University Psychiatric Hospital Vrapče in Zagreb, July 2012 to December \n2014. The diagnosis of schizophrenia was set by two independent psychiatrists \naccording to diagnostic criteria of ICD-10 and DSM-IV. \nFor each patient socio-demographic and hospital data were gathered. Observed \nclinical features were objectified using PANSS, CGI, ISMI (Internalized stigma in \nMental Illness), SUMD (Scale to Assess Unawareness of Mental Disorder), CDSS \n(Calgary Depression Scale for Schizophrenia), ISST (InterSePT Scale for Suicidal \nThinking), BCS (Beck's Hopelessness Scale), and WHOQOL-BREF (WHO Quality of \nLife-BREF). \nRegarding insight into illness statistically significant difference was found \nbetween in the intensity of psychopathology, general clinical impression, depression, \nsuicidality, hopelessness and quality of life. Regarding internalized selfstigma \nstatistically significant difference was found between in the intensity of \npsychopathology, general clinical impression, depression, suicidality, hopelessness and \nquality of life. \nThe study confirmed hypothesis that internalized selfstigma moderates \nrelationship between better insight into illness and depression. The hypothesis that \ngreater insight into the disease and internalized selfstigma has an impact on depression, \nsuicide, hopelessness and lower quality of life. Moderating role of internalized \nselfstigma on relationship between insight into illness and suicidality was not found.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".