03 - How well do patients with schizophrenia deflect public stigma? Findings from a Nigerian cohort.
Bibliographic record
Abstract
CONGRESS TOPIC 76. Stigma and Mental IllnessPRESENTER: Dr. Vahe Kehyayan, PhDvkehyaya@ucalgary.ca+974-6624-8396AUTHOR AND CO-AUTHORS Lead authorDr. Vahe Kehyayan, PhDUniversity of Calgary in QatarP.O.Box 23133Doha, Qatarvkehyaya@ucalgary.ca+974-6624-8396 Co-authorsDr. Ziyad Mahfoud, PhDWeil Cornell Medical College in QatarDoha, Qatar Dr. Suhaila Ghuloum, MDMental Health ServicesHamad Medical CorporationDoha, QatarMrs. Tamara Marji, MSc.University of Calgary in QatarDoha, QatarDr. Hassen Al Amin, MDWeil Cornell Medical College in QatarDoha, Qatar TITLE: INTERNALIZED STIGMA IN PERSONS WITH MENTAL ILLNESS AND THEIR FAMILIES IN QATAR: A CROSS-SECTIONAL MIXED METHODS STUDYObjectives: to assess the levels of stigma in persons with mental illness (PWMI) and their families; to explore PWMIu2019s lived experiences; to examine the association of levels of stigma with participantsu2019 background characteristics.Background and Aims: Stigma is a major barrier for PWMI to seek treatment. Untreated mental illness contributes to the burden of disease, disability and mortality. Studies have shown that interventions to address stigma can help improve the overall recovery and re-integration of PWMI into the community. The aim of this study was to inform the development of strategic interventions to address stigma.Materials and Methods: The Internalized Stigma Mental Illness (ISMI) scale was used to interview patients and an adapted version for family members. Significance was set at p<0.05. Results: In multivariate logistic regression, patients with some level of education were less likely to report high stigma (>2.5) compared to no formal education, and those with lower level of social support, the higher the odds of high stigma. Family members who were married reported significantly higher levels of stigma (>2.0) and those who had at least college education reported significantly lower levels stigma. Conclusions: Internalized stigma is common in these vulnerable populations. An anti-stigma education program with targeted audiences in the context of Qatar may be conducive to creating an all-inclusive society to encourage PWMI to voluntarily self-disclose their mental illness and to seek early intervention from the formal healthcare system, and to encourage families to support their family members in their journey to recovery.Key words: stigma; internalized stigma; self-stigma; Qatar; Internalized Stigma Mental Illness Scale
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".