The use of alcohol and drugs to self-medicate symptoms of posttraumatic stress disorder
Bibliographic record
Abstract
BACKGROUND: Self-medication has been proposed as an explanation for the high rates of comorbidity between posttraumatic stress disorder (PTSD) and substance use disorders; however, knowledge of self-medication in PTSD is scarce. We describe the prevalence and correlates of self-medication in PTSD in the general population. METHODS: Data came from the National Epidemiologic Survey on Alcohol and Related Conditions Wave 2 (N=34,653; response rate: 70.2%), a nationally representative survey of mental illness in community-dwelling adults. Self-medication was assessed separately for alcohol and drugs. Prevalence rates were determined for self-medication among individuals with DSM-IV PTSD. Regression analyses determined associations between self-medication and a variety of correlates, including sociodemographic factors, comorbid mental disorders, suicide attempts, and quality of life. RESULTS: Approximately 20% of individuals with PTSD used substances in an attempt to relieve their symptoms. Men were significantly more likely than women to engage in self-medication behavior. In adjusted models, using illicit drugs or misusing prescription medications to control PTSD symptoms was associated with a substantially higher likelihood of dysthymia and borderline personality disorder. After controlling for mental disorder comorbidity, self-medication was independently associated with higher odds of suicide attempts (adjusted odds ratio=2.46; 95% confidence interval 1.53-3.97) and lower mental health-related quality of life. CONCLUSIONS: Self-medication is a common behavior among people with PTSD in the community, yet has potentially hazardous consequences. Health care practitioners should assess reasons for substance use among people with PTSD to identify a subgroup with higher psychiatric morbidity.
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 | 0.002 |
| 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) | 0.004 | 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".