Internalized stigma and resistance against stigma as risk factors for suicide in individuals with schizophrenia: a cross-sectional study
Bibliographic record
Abstract
Introduction: Identifying the relationship between internalized stigma and suicide is critical in dealing with schizophrenia. We aimed to examine how internalized stigma and its subcomponents affect suicidality in patients with schizophrenia. The second aim of this study was to identify the risk factors for internalized stigma in schizophrenia. Methods: We assessed 114 patients diagnosed with schizophrenia. Structured Clinical Interview for the DSM-5 (SCID-5), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale (CDS), Internalized Stigma of Mental Illness (ISMI), and Suicide Probability Scale (SPS) were applied to the sample. Multivariable linear regression analysis was conducted to determine the risk factors of internalized stigma. Results: Statistically significant correlation between stigma resistance and all SPS scores was found. The correlation between stigma resistance and suicidal thought was independent of the CDS and PANSS scores of the sample. Stigma resistance and depressive situation were the predictive factors for SPS. Only depressive state of the group predicted the level of internalized stigma in the regression analysis. Conclusions: Stigma resistance is an important risk factor for suicide in schizophrenia. Clinicians should focus on interventions to increase resistance against stigma and to determine the depressive situation of patients with schizophrenia.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".