The experience and impact of stigma in Saudi people with a mood disorder
Bibliographic record
Abstract
Introduction Self-stigma plays a powerful role in attitudes toward mental illness and seeking psychological services. Assessing stigma from the perspective of people with mood disorders is important as they were ranked as major causes of disability. Objectives To determine the extent and the impact of stigma experience in Saudi patients with mood disorder and compare them between depression and bipolar disorder patients. To test if stigma is a universal experience and has similar psychosocial impact across cultures. Aim It's a part of multicenter international study comparing its results to the universal experiences inthe perspectives of individuals with mood disorder. Methodology We randomly interviewed 94 individuals with mood disorder at King Khalid University Hospital using valid reliable tool, Inventory of Stigmatizing Experiences (ISE), which has two components: Stigma Experiences Scale (SES) and Stigma Impact Scale (SIS). Results ISE was validated in a population of Saudi patients with mood disorder. There were no significant differences in stigma between patients with bipolar or depressive disorder on SES or SIS. However, over 50% of all respondents tried to hide their mental illness from the others, and to avoid situations that might lead them to be stigmatized. In comparison with the Canadian population, Saudi participants scored lower on both SES and SIS, which may be due to cultural differences. Conclusion Stigma associated with mood disorder is serious and pervasive. It's important first to understand how patients perceive stigma in order to conduct successful anti-stigma programs. The ISE is a highly reliable instrument among cultures. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".