Stigma Resistance in Stable Schizophrenia: The Relative Contributions of Stereotype Endorsement, Self-Reflection, Self-Esteem, and Coping Styles
Bibliographic record
Abstract
OBJECTIVE: Stigma resistance (SR) has recently emerged as a prominent aspect of research on recovery from schizophrenia, partly because studies have suggested that the development of stigma-resisting beliefs may help individuals lead a fulfilling life and recover from their mental illness. The present study assessed the relationship between personal SR ability and prediction variables such as self-stigma, self-esteem, self-reflection, coping styles, and psychotic symptomatology. METHOD: We performed an exploratory cross-sectional study of 170 community-dwelling patients with schizophrenia. Self-stigma, self-esteem, self-reflection, coping skills, and SR were assessed through self-report. Psychotic symptom severity was rated by the interviewers. Factors showing significant association in univariate analyses were included in a stepwise backward regression model. RESULTS: Stepwise regressions revealed that acceptance of stereotypes of mental illness, self-esteem, self-reflection, and only 2 adaptive coping strategies (positive reinterpretation and religious coping) were significant predictors of SR. The prediction model accounted for 27.1% of the variance in the SR subscale score in our sample. CONCLUSIONS: Greater reflective capacity, greater self-esteem, greater preferences for positive reinterpretation and religious coping, and fewer endorsements of the stereotypes of mental illness may be key factors that relate to higher levels of SR. These factors are potentially modifiable in tailored interventions, and such modification may produce considerable improvements in the SR of the investigated population. This study has implications for psychosocial rehabilitation and emerging views of recovery from mental illness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".