Self-help group (SHG) attendance and treatment outcomes among older adults in the US
Bibliographic record
Abstract
Substance dependency is a global problem and significantly affects the geriatric population in the United States. This study aims to determine how self-help group attendance affects substance use treatment outcomes among older adults in the US. This cross-sectional study used the 2020 discharge treatment episodes data set (TEDS-D) from the Substance Abuse and Mental Health Services Administration (SAMHSA). Multivariable logistic regression was used to evaluate the relationship between self-help group (SHG) attendance and treatment outcomes among older adults. We included 3,424 older adults (19.2% female). The primary substance use was alcohol in more than two-thirds of the participants (67.9%), while heroin (17.1%), cocaine (5.8%), and other opiates/synthetics (3.3%) were the other common primary substance of abuse among other participants. In the multivariate logistic regression analysis, SHG attendance at discharge from treatment facility was significantly associated with reduced frequency of use of primary substance -FUPS (p-value = 0.013) and increased odds of treatment completion (p-value <0.001) but no significant association with arrests at discharge from treatment facility (p-value = 0.101). SHG attendance on admission into treatment facility was associated with reduced odds of treatment completion (p-value <0.001). Having a living arrangement at discharge was found to be associated with reduced FUPS (p-value <0.001) but with lower odds of treatment completion (p-value <0.001). Association of SHG attendance with positive treatment outcomes indicates the need to enhance access to this service in the geriatric population.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".