Posttraumatic stress disorder and substance use disorder comorbidity in homeless adults: Prevalence, correlates, and sex differences.
Bibliographic record
Abstract
Substance use disorders (SUDs) are highly prevalent in homeless populations, and rates are typically greater among males. Posttraumatic stress disorder (PTSD) is a common co-occurring condition among individuals with SUDs; however, little attention has been directed to examining this comorbidity in homeless populations. Although some studies indicate considerable sex differences among individuals with PTSD, it has also been suggested that sex differences in PTSD rates diminish in populations with severe SUDs. This cross-sectional study investigated SUD-PTSD comorbidity and its associations with indicators of psychosocial functioning in a sample of 500 homeless individuals from Canada. Sex-related patterns of SUD, PTSD, and their comorbidity were also examined. Males and females had similar SUD prevalence rates, but the rates of PTSD and PTSD-SUD comorbidity were higher in females. PTSD and sex were found to have significant main effects on suicidality, psychological distress, somatic symptoms, and incarceration among individuals with SUD. Sex also moderated the association of PTSD with suicide risk and psychological distress. Our results contradict assumptions that sex differences in PTSD rates attenuate in samples with severe SUDs. Organizations providing SUD treatment for homeless people should address PTSD as an integrated part of their services. SUD and integrated treatment programs may benefit from sex-specific components.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".