Sex differences among treatment clients with cocaine-related problems
Bibliographic record
Abstract
Background: In a sample of treatment clients with cocaine-related problems, the present study examined sex differences in measures across six key domains, including socio-demographics, mental health, substance use, physical health, sexual health and psychosocial health.Methods: Data were utilized from a cross-sectional study of treatment clients in Ontario and British Columbia, Canada (N = 417). t-Tests were used to examine sex differences in continuous measures, while Fisher’s exact tests were used for dichotomous measures. A Bonferroni correction was applied to adjust for multiple comparisons. For measures that were significant in these tests, multivariable analyses were also conducted.Results: Females were found to be more likely than males to have lower personal and household incomes, report membership in sexual minority groups and engage in high risk sexual behaviors, including trading sex for money, trading sex for drugs and having sex when they did not want to. Males were more likely than females to report higher sexual compulsion scores and have paid for sex.Conclusion: Overall, the health-related needs of treatment clients with cocaine-related problems appear to differ by sex, especially in relation to sexual health. As such, setting of treatment priorities by treatment providers should reflect these important differences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".