International monitoring of capacity of treatment systems for alcohol and drug use disorders: Methodology of the Service Capacity Index for Substance Use Disorders
Bibliographic record
Abstract
OBJECTIVES: We aimed to develop a Service Capacity Index for Substance Use Disorders (SCI-SUD) that would reflect the capacity of national health systems to provide treatment for alcohol and drug use disorders, in terms of the proportion of available service elements in a given country from a theoretical maximum. METHODS: Data were collected through the WHO Global Survey on Progress with Sustainable Development Goals (SDG) Health Target 3.5, conducted between December 2019 and July 2020 to produce the SCI-SUD, based on 378 variables overall. RESULTS: The SCI-SUD was directly derived for 145 countries. We used multiple imputation to produce comparable SCI-SUD estimates for countries that did not submit data (40 countries) or had very high level of missingness (9 countries). The final SCI-SUD demonstrates considerable consistency and internal stability and is strongly associated with the macro-level economic, healthcare-related and epidemiologic (such as prevalence rates) variables. CONCLUSION: The presented methodology represents a step forward in monitoring the global situation in regard to the development of treatment systems for SU disorders, however, further work is warranted to improve the external validity of the measure (e.g., in-depth data generation in countries) and ensure its feasibility for regular reporting (e.g., reducing the number of variables).
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".