The relationships between sociodemographic, psychosocial and clinical variables with personal-stigma in patients diagnosed with schizophrenia.
Bibliographic record
Abstract
BACKGROUND: Studies suggest that people with a diagnosis of schizophrenia are one of the most stigmatized groups in society. AIM: To comprehensively analyze personal stigma in patients diagnosed with schizophrenia. METHOD: Data were obtained from 89 patients. Patients were evaluated with the following scales: a sociodemographic and clinical questionnaire, the Discrimination and Stigma Scale, the Self-perception of Stigma Questionnaire for People with Schizophrenia, the Positive and Negative Syndrome Scale, the Calgary Depression Scale for Schizophrenia, the Global Assessment of Functioning Scale, and the Brief Social Functioning Scale. RESULTS: Relations between personal stigma and sociodemographic and psychosocial variables were poor. However, clinical variables correlated with different facets of personal stigma. Personal stigma subscales´ correlations were between experienced stigma, anticipated stigma, and self-stigma to each other. 29.5% of the experienced stigma subscale variance was explained by age of onset and level of depression. 20.1% of the anticipated stigma subscale variance was explained by level of depression and gender. 27.3% of the overcoming stigma subscale variance was explained by level of depression and positive and negative psychotic symptoms. 35.8% of the self-stigma scale variance was explained by the level of depression. CONCLUSIONS: Addressing stigma within treatment seems of crucial importance since all stigma facets seem to be highly related to clinical dimensions, especially depression Therefore, including strategies to reduce stigma in care programs may help patients with schizophrenia to better adjust in life and improve their illness process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".