Intersectoral Interventions in School to Develop Strategies to Prevent the Use of Alcohol and Other Drugs: A Scoping Review
Bibliographic record
Abstract
It is important to prioritize intersectoral action at schools to prevent the use of alcohol and other drugs. This strategic act should be organized with multidisciplinary learning characteristics and with the involvement of different stakeholders. The aim of a recent scoping review was to identify the factors that benefit and hinder intersectoral actions at school resulting from the elaboration of policies to prevent drug use. It seems like the research study was a scoping review that used the P-population, C-context, and C-concept structure. The study searched for information in various databases like Embase, Proquest, Lilacs, Medline via Pubmed, PsycInfo, WHO-Iris, and PAHO-Iris on April 17, 2023. According to the results, there were 5 studies that were eligible to answer the research question. One of the advantages of intersectoral actions is the role of schools in creating a support network with different social actors, particularly with family involvement. On the other hand, one of the challenges is inconsistencies in legal regulations, which do not provide enough guidance to schools on how to prevent alcohol and drug use. While public and private schools may be affected differently by social and economic factors, it is essential to invest in developing policies that focus on drug prevention for children and adolescents who are learning in schools.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".