Predictors of Suicidal Ideation Among “At Risk” Cocaine‐Using African American Women
Bibliographic record
Abstract
The focus of this study was to identify factors that differentiate women drug abusers who had experienced suicidal ideation from those who did not. Face-to-face interviews were conducted with 221 cocaine-using women in Atlanta, GA, 88 (39.8%) of whom reported thinking about committing suicide at least once during the 90 days prior to interview and 133 (60.2%) of whom did not. Multivariate logistic regression was used to identify predictors of suicidal ideation, and post hoc goodness-of-fit tests were conducted to assess the robustness of final models derived. Model 1 excluded all psychosocial functioning measures and Model 2 included these items. Both models showed that suicidal ideation was more common among women who were unemployed, had been abused sexually, engaged in sexual relations to cope with stresses, or had less helpful relatives. In addition, Model 1 revealed a heightened risk for women experiencing financial problems and those who had a previous mental health diagnosis, whereas Model 2 showed an elevated risk among women who experienced anxiety and those who had lower levels of self-esteem. These findings suggest the need for prevention and intervention programs that target at-risk women, and for such programs to include an emphasis on suicidal ideation in addition to focusing on risk factors that are addressed more commonly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".