Estimating Service Needs for Alcohol and Other Drug Users According to a Tiered Framework: The Case of the São Paulo, Brazil, Metropolitan Area
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to estimate the need for population-level services for alcohol and other drug abuse in support of local planning. METHOD: Data were drawn from a subsample of 2,942 interviewees from the São Paulo Megacity Study, which evaluated mental health in the general population (18 years and older) of residents in the São Paulo metropolitan area. This population was classified into five hierarchical categories of severity, making it possible to obtain estimates of need for services, combining evaluation criteria regarding drug and alcohol use and general and mental health comorbidities over the last 12 months. For the at-risk groups in this population, estimates from the Potential Demand for the Use of Services survey interviews over the last year were generated. RESULTS: Concerning the need for services, 86.5% of the population (Tier 1) had no problems related to drug and alcohol use, 8.9% (Tier 2) used heavily, 3.5% (Tiers 3, 4, and 5) met criteria for substance abuse disorders, among whom 1.3% (Tiers 4 and 5) require more specialized and intensive treatment and support. The following estimates for the Potential Demand for the Use of Services were found: 25.5% (Tier 3) and 51.1% (Tier 4), indicating that a significant number of individuals met criteria for substance abuse disorders but did not perceive any need for professional help or neglected the help available. CONCLUSIONS: In São Paulo there exists a large sector of the population that requires prevention strategies regarding the risks and harm resulting from alcohol and drug use, followed by a group requiring more specialized care. But a large number of substance users requiring specialized support did not use services and did not believe that they needed professional help.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".