Implementation of public policy on alcohol and other drugs in Brazilian municipalities: comparative studies
Bibliographic record
Abstract
One of the challenges with respect to public health and the abuse of alcohol and other drugs is to implement policies in support of greater co-ordination among various levels of government. In Brazil, policies are formulated by the Secretaria Nacional de Políticas sobre Drogas (SENAD - State Department for Policies on Drugs) and the Ministério da Saúde (MS - Ministry of Health). This study aims to compare implementation of policies adopted by SENAD and MS at the municipal level. Three municipalities were intentionally selected: Juiz de Fora having a larger network of treatment services for alcohol and drug users; Lima Duarte, a small municipality, which promotes the political participation of local actors (COMAD - Municipal Council on Alcohol and Drugs); and São João Nepomuceno, also a small municipality, chosen because it has neither public services specialised to assist alcohol and other drugs users, nor COMAD. Data collection was conducted through interviews with key informants (n = 19) and a review of key documents concerned with municipal policies. Data analysis was performed using content analysis. In Juiz de Fora, there are obstacles regarding the integration of the service network for alcohol and other drug users and also the articulation of local actors, who are predominant in the mental health sector. In Lima Duarte, while there is a link between local actors through COMAD, their actions within the local service network have not been effective. In São João Nepomuceno, there were no public actions in the area of alcohol and drugs, and consequently insufficient local debate. However, some voluntary, non-governmental work has been undertaken. There were weaknesses in the implementation of national-level policies by SENAD and the MS, due to the limited supply of available treatment, assistance and the lack of integration among local actors.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".