Implementing intersectoral alcohol policies at the local level: A case study from Santiago, Chile, 2014-2017
Bibliographic record
Abstract
Background. Local governments have a crucial role to play in alcohol control policies. However, there is a lack of descriptions of comprehensive intersectoral alcohol control strategies led by them. The study describes the experience of developing and implementing an intersectoral alcohol strategy in the Municipality of Santiago, Chile, between 2014-2017. Methods. We used a case study design. We used data from municipal documents, including reports from fee agreements, official sources of information, municipal service calls and police records, georeferenced data on alcohol outlets and photographs of storefront signs. We used data from interviews with community stakeholders and municipal workers conducted during the study period. Results. The first stage (2014-2015) consisted of using local evidence to build political will. The main activities were introducing screening and brief alcohol interventions in high schools, supporting a public consultation on reducing the opening hours of liquor stores, and introducing economic incentives to reduce street-level alcohol marketing. The second stage (2015-2017) included a community-action pilot plan and the development and implementation of an intersectoral alcohol control plan involving twelve municipal departments. Activities aimed at reducing the number of alcohol outlets, enhancing transparency on alcohol licensing procedures, and improving the quality of brief interventions. The strategy implemented actions in nine out of ten WHO Alcohol Policy domains. Conclusions. The experience of Santiago demonstrates the untapped potential for alcohol control at the local level. Political will, local evidence, sharing common goals and medium-term budget frameworks are important facilitators of comprehensive intersectoral alcohol interventions.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Case study of municipal alcohol control policy implementation; health policy, not research policy.
The work evaluates local alcohol-control policy implementation in Santiago.
Case study of local alcohol-control policy implementation in Chile; public health policy, not research practice.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".