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Record W4391538986 · doi:10.1177/00207640231221090

Self-stigma, religiosity, and perceived social support in people with recent-onset psychosis in the Islamic Republic of Iran: Associations with symptom severity and psychosocial functioning

2024· article· en· W4391538986 on OpenAlexaboutno aff
Matej Djordjevic, Sara Farhang, Mohammad Reza Shirzadi, S. Bentolhoda Mousavi, Richard Bruggeman, Ayyoub Malek, Arash Mohagheghi, Fatemeh Ranjbar, AR Shafiee-Kandjani, HE Jongsma, Wim Veling

Bibliographic record

VenueInternational Journal of Social Psychiatry · 2024
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
FundersNational Institute for Medical Research DevelopmentUniversitair Medisch Centrum Groningen
KeywordsReligiosityPsychosocialPsychosisSocial functioningIslamic republicPsychologyPsychiatrySocial supportClinical psychologySocial stigmaStigma (botany)IslamMedicineDistressPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

Aims: Most evidence on psychosocial factors in recent-onset psychosis comes from high-income countries in Europe, Australia, Canada and the USA, while these factors are likely to differ under varying sociocultural and economic circumstances. In this study, we aimed to investigate associations of self-stigma, religiosity and perceived social support with symptom severity and psychosocial functioning in an Iranian cohort of people with recent-onset psychosis (i.e. illness duration of <2 years). Methods: We used baseline data of 361 participants ( N = 286 [74%] male, mean age = 34 years [Standard Deviation = 10.0]) from the Iranian Azeri Recent-onset Acute Phase Psychosis Survey (ARAS). We included assessments of self-stigma (Internalized Stigma of Mental Illness, ISMI), religiosity (based on Stark & Glock), perceived social support (Multidimensional Scale of Perceived Social Support, MSPSS), symptom severity (Positive And Negative Syndrome Scale, PANSS) and psychosocial functioning (clinician-rated Global Assessment of Functioning Scale, GAF, and self-reported World Health Organization Disability Assessment Schedule 2.0, WHODAS 2.0). Descriptive analyses were employed to characterize the study sample. Covariate-adjusted ordinal and multivariable linear regression analyses were performed to investigate cross-sectional associations of baseline ISMI, religiosity and MSPSS with concurrent PANSS, GAF and WHODAS 2.0. Results: Higher self-stigma was associated with poorer self-reported functioning ( B = 0.375 [95% Confidence Interval (CI): 0.186, 0.564]) and more severe concurrent symptoms ( B = 0.436 [95% CI: 0.275, 0.597]). Being more religious was associated with poorer clinician-rated functioning (OR = 0.967 [95% CI: 0.944, 0.991]), but with less severe symptoms ( B = −0.258 [95% CI: −0.427, −0.088]). Stronger social support was associated with poorer clinician-rated (OR = 0.956 [95% CI: 0.935, 0.978]) and self-reported functioning ( B = 0.337 [95% CI: 0.168, 0.507]). Conclusion: This study shows that self-stigma, religiosity and perceived social support were associated with symptom severity and clinician-rated as well as self-reported psychosocial functioning in an Iranian cohort of people with recent-onset psychosis. The findings extend previous evidence on these psychosocial factors to one of the largest countries in the Middle East, and suggest that it may be worthwhile to develop strategies aimed at tackling stigma around psychosis and integrate the role of religiosity and social support in mental ill-health prevention and therapy.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.307
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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