Community-based interventions and alcohol, tobacco and other drugs: foci, outcomes and implications
Bibliographic record
Abstract
The social, health and economic burdens from alcohol, tobacco and other drugs have impacts globally, national and locally. Effective interventions are needed at each level in order to reduce the extensive harm and attendant costs. This paper examines four topics: options available to the local community, evidence of effectiveness, links between local experiences and national and regional initiatives and implications for future research and intervention. It appears that there are a substantial number of options available at the local level. However, evaluation of them is not standard practice, and the results of the higher quality evaluations indicate that many, but not all, interventions have modest or equivocal impact. There is also not a consistent relationship between local and national interventions, although some themes are apparent: in tobacco control there may be good synergy across jurisdictional levels, for alcohol there is evidence that as national control measures are eroded local communities are encouraged or required to take up these agendas, and with regard to illicit drugs there may be tension between law enforcement priorities at the national level and harm reduction orientations locally. Future initiatives need to have appropriate evaluations as a standardised part of prevention initiatives, and include the development of national databases of what is going on locally. These initiatives should promote national policies that include setting parameters and guidelines, but nevertheless do not dictate specific steps and strategies how to achieve local goals in reducing risk and harm.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".