Scaling-up primary health care-based prevention and management of heavy drinking at the municipal level in middle-income countries in Latin America: Background and protocol for a three-country quasi-experimental study
Bibliographic record
Abstract
Background : While primary health care (PHC)-based prevention and management of heavy drinking is clinically effective and cost-effective, it remains poorly implemented in routine practice. Systematic reviews and multi-country studies have demonstrated the ability of training and support programmes to increase PHC-based screening and brief advice activity to reduce heavy drinking. However, gains have been only modest and short term at best. WHO studies have concluded that a more effective uptake could be achieved by embedding PHC activity within broader community and municipal support. Protocol : A quasi-experimental study will compare PHC-based prevention and management of heavy drinking in three intervention cities from Colombia, Mexico and Peru with three comparator cities from the same countries. In the implementation cities, primary health care units (PHCUs) will receive training embedded within ongoing supportive municipal action over an 18-month implementation period. In the comparator cities, practice as usual will continue at both municipal and PHCU levels. The primary outcome will be the proportion of consulting adult patients intervened with (screened and advice given to screen positives). The study is powered to detect a doubling of the outcome measure from an estimated 2.5/1,000 patients at baseline. Formal evaluation points will be at baseline, mid-point and end-point of the 18-month implementation period. We will present the ratio (plus 95% confidence interval) of the proportion of patients receiving intervention in the implementation cities with the proportions in the comparator cities. Full process evaluation will be undertaken, coupled with an analysis of potential contextual, financial and political-economy influencing factors. Discussion : This multi-country study will test the extent to which embedding PHC-based prevention and management of alcohol use disorder with supportive municipal action leads to improved scale-up of more patients with heavy drinking receiving appropriate advice and treatment. Study status : The four-year study will start on 1 st December 2017.
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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 | 0.051 | 0.037 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.012 |
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".