The Role of Gender in Suicidal Ideation among Long-term Opioid Users
Bibliographic record
Abstract
OBJECTIVE: This study aims to examine factors associated with suicidal ideation among people with opioid dependence and to explore whether these factors are gender-specific. METHODS: Cross-sectional data were collected among long-term opioid-dependent individuals ( n = 176; 46.0% women). Lifetime histories of suicidal ideation were measured using the Composite International Diagnostic Interview, and additional data were collected regarding sociodemographic characteristics, drug use, health, and adverse life events. Multivariable logistic regression was used to determine the relationships between these variables and suicidal ideation for the full study sample and separately for women and men to explore the potential role of gender. RESULTS: A total of 43.8% ( n = 77) of participants reported a lifetime history of suicidal ideation. Among those with suicidal ideation, 49.3% were women and the overall average age of first ideation was 19.82 years (SD, 11.66 years). Results from multivariable analyses showed that a history of depression, anxiety, and childhood emotional neglect and the number of lifetime traumatic events were significantly associated with higher odds of suicidal ideation. The gender-based analysis suggested that histories of depression and anxiety remained independently associated with lifetime suicidal ideation among women, whereas for men, childhood emotional neglect and the number of lifetime potentially traumatic events were independently associated with lifetime suicidal ideation. CONCLUSIONS: This study offers a critical first step to understanding factors associated with suicidal ideation among long-term opioid-dependent men and women and the potential importance of gender-sensitive approaches for suicidal behavior interventions. These data inform further research and clinical opportunities aiming to better respond to the psychological health needs of this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".