Religious Institutions as a Link to Substance use Treatment: Characterizing the Potential Service Population through National Survey Data
Bibliographic record
Abstract
Background: Relatively few Americans with current alcohol or drug use disorders receive outpatient or residential treatment. Outreach initiatives at local places of religious worship have been proposed as a way of facilitating such service use, but the number and characteristics of adults who may be reached in this way has not been studied. Methods: Data from the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions-III, a nationally representative cross-sectional survey of U.S. adults were used to estimate the number of and proportion of adults with substance use disorders (SUDs) who attended monthly religious service and did not receive SUD treatment in the past year and used multinomial logistic regression to compare them to three SUD groups who did or did not receive treatment and/or attend religious services. Results: A total of 5,795 respondents representing 35.8 million Americans met criteria for a past-year SUD, of whom 8.3 million (23.1%) attended religious services monthly and did not receive substance use treatment. This more often African-American group had substantially fewer socio-demographic disadvantages (e.g., unemployment), behavioral problem indicators (e.g., police involvement), a higher quality of life score and less likelihood of an illicit drug use diagnosis than those who received treatment and either did or did not attend religious services. Conclusion: Almost one quarter of adults with a SUD attend religious services monthly and do not receive SUD treatment. Although they have fewer adversities than people who receive treatment, outreach to this population may link this substantial group of people to needed services. Highlights/review National survey data suggest 8 of 36 million Americans with substance use diagnoses’ (23%) do not receive specialized SUD treatment, but they do attend religious services monthly or more. This group, notably, has less numerous problems, such as unemployment, police involvement, and drug use disorder, and have higher quality of life scores than those who receive treatment for SUD. Outreach and linkage initiatives with religious institutions may facilitate use of services by this 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".