Costs of an Alcohol Measurement Intervention in Three Latin American Countries
Bibliographic record
Abstract
Alcohol measurement in health care settings is an effective intervention for reducing alcohol-related harm. However, in many countries, costs related to alcohol measurement have not yet been transparently assessed, which may hinder its adoption and implementation. Costs of an alcohol measurement programme in three upper-middle-income Latin American countries were assessed via questionnaires and compared, as part of the quasi-experimental SCALA study. Additional to the intervention costs, the costs of three implementation strategies: standard training and clinical package, intensive training and clinical package, and community support, were assessed and subsequently translated into costs per additional alcohol measurement session. Results demonstrated that costs for one alcohol measurement session ranged between Int$ 0.67 and Int$ 1.23 in Colombia, Int$ 1.19 and Int$ 2.57 in Mexico, and Int$ 1.11 and Int$ 2.14 in Peru. Costs were mainly driven by the salaries of the health professionals. Implementation strategies costs per additional alcohol measurement session ranged between Int$ 1.24 and Int$ 6.17. In all three countries, standard training and a clinical package may be a promising implementation strategy with a relatively low cost per additional alcohol measurement session.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".