Demographic and clinical correlates of suicidal ideation in individuals with at‐risk mental state (<scp>ARMS</scp>): A study from Pakistan
Bibliographic record
Abstract
BACKGROUND: Suicide is a major public health concern and one of the leading causes of mortality worldwide. People with an at-risk-mental-state (ARMS) for psychosis are more vulnerable to psychiatric co-morbidity and suicide, however, there are limited data from low-middle-income countries. The present study aimed to identify the prevalence of depressive symptoms and suicidal ideation along with sociodemographic and clinical correlates of suicidal ideation in individuals with ARMS from Pakistan. METHOD: Participants between the age of 16 and 35 years who met the criteria for ARMS based on the Comprehensive Assessment of At-Risk Mental State (CAARMS), were recruited from the community, general practitioner clinics and psychiatric units across Pakistan (n = 326). Montgomery and Asberg Depression Rating Scale (MADRS) and Social-Occupational-Functional-Assessment-Scale (SOFAS) were administered to participants. RESULTS: The prevalence of depressive symptoms and suicidal thoughts in the sample at baseline were 91.1% (n = 297) and 61.0% (n = 199), respectively. There were significant mean differences between groups (mean difference [95% CI]; p-value) without suicidal ideation and with suicidal ideation on measures of MADRS (-5.47 [-7.14, -3.81]; p < .001), CAARMS non-bizarre ideas (-0.29 [-0.47, -0.11]; p = .002) and perceptual abnormalities (-0.23 [-0.41, -0.04]; p = .015). CONCLUSION: These findings indicate that suicidal ideation and depressive symptoms are highly prevalent in individuals with ARMS in Pakistan. Given the pivotal developmental stages that ARMS presents, and the poor outcomes associated with co-morbid depression, there is an urgent need to prioritize the development of low-cost and scalable evidence-based interventions to address psychiatric comorbidity and suicidality in the ARMS population in Pakistan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".