Impact of Training and Municipal Support on Primary Health Care–Based Measurement of Alcohol Consumption in Three Latin American Countries: 5-Month Outcome Results of the Quasi-experimental Randomized SCALA Trial
Bibliographic record
Abstract
We aimed to test the effects of providing municipal support and training to primary health care providers compared to both training alone and to care as usual on the proportion of adult patients having their alcohol consumption measured.We undertook a quasi-experimental study reporting on a 5-month implementation period in 58 primary health care centres from municipal areas within Bogotá (Colombia), Mexico City (Mexico), and Lima (Peru). Within the municipal areas, units were randomized to four arms: (1) care as usual (control); (2) training alone; (3) training and municipal support, designed specifically for the study, using a less intensive clinical and training package; and (4) training and municipal support, designed specifically for the study, using a more intense clinical and training package. The primary outcome was the cumulative proportion of consulting adult patients out of the population registered within the centre whose alcohol consumption was measured (coverage).The combination of municipal support and training did not result in higher coverage than training alone (incidence rate ratio (IRR) = 1.0, 95% CI = 0.6 to 0.8). Training alone resulted in higher coverage than no training (IRR = 9.8, 95% CI = 4.1 to 24.7). Coverage did not differ by intensity of the clinical and training package (coefficient = 0.8, 95% CI 0.4 to 1.5).Training of providers is key to increasing coverage of alcohol measurement amongst primary health care patients. Although municipal support provided no added value, it is too early to conclude this finding, since full implementation was shortened due to COVID-19 restrictions.Clinical Trials.gov ID: NCT03524599; Registered 15 May 2018; https://clinicaltrials.gov/ct2/show/NCT03524599.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".