Alcohol and drug use among clients receiving internet-delivered cognitive behavior therapy for anxiety and depression in a routine care clinic – Demographics, use patterns, and prediction of treatment completion and outcomes
Bibliographic record
Abstract
BACKGROUND: Research shows that alcohol and drug use among mental health clients is common and has the potential to negatively impact treatment outcomes. Internet-delivered cognitive behavior therapy (ICBT) as a treatment for anxiety and depression is on the rise, but little is known about the prevalence of alcohol and drug use among clients and how this use affects treatment completion and outcomes. OBJECTIVE: The objective of the current study was to explore the prevalence of alcohol and drug use among clients in ICBT for depression and anxiety, and to investigate the impact of alcohol and drug use on treatment completion and symptom outcomes. MATERIAL AND METHODS: Data was collected from 1155 clients who participated in two randomized ICBT trials for depression and anxiety, conducted in a routine care clinic. Thirty-five individuals reporting severe substance use when applying to the trials were excluded. Demographic variables, and alcohol and drug use were measured at screening, and measures of depression and anxiety were administered at pre- and post-treatment. RESULTS: Four out of five clients reported having used alcohol in the past year, while one in five reported having used drugs in the past year. Around a third of clients had reported either problematic alcohol use, drug problems, or both. The analyses showed that drug problems, and combined alcohol and drug problems were negatively associated with treatment completion, but neither alcohol nor drug use had an impact on depression and anxiety outcomes. CONCLUSIONS: Alcohol and drug problems are likely to be present among a large proportion of patients using ICBT for anxiety and depression. This may not be a barrier to treatment benefit, at least when those with severe alcohol and drug problems have been excluded.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".