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 (SHG) 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)fromthe Substance Abuse and Mental Health Services Administration (SAMHSA). Multivariable logistic regression was used to evaluate the relationship between self-help group 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. • The primary substances used were alcohol, heroin, and cocaine in 45.8%, 18.2%, and 4.9% of participants. • Self-help group attendance at dischargefrom treatment facility was significantly associated with reduced frequency of use of primary substance and increased odds of treatment completion. • Having a living arrangement at discharge was found to be associated with lower odds of treatment completion among older adults. Substance use is a prevalent problem among older adults in the United States ( Lipari, 2018 ). According to the Substance Abuse and Mental Health Services Administration (SAMHSA), approximately 5.7 million older adults had a form of substance use disorder (SUD) in 2018, and this number is expected to increase to nearly 6.4 million by 2030 ( Lipari, 2018 ). SUD can have severe negative consequences on older adults' physical and mental health, leading to a higher risk of chronic illnesses, cognitive impairment, and social isolation ( Meier & Best, 2006 ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".