Protocol for a process evaluation of SCALA study – Intervention targeting scaling up of primary health care-based prevention and management of heavy drinking and comorbid depression in Latin America
Bibliographic record
Abstract
This paper describes the plan for a process evaluation of a quasi-experimental study testing the municipal level scale-up of primary health care-based measurement and brief advice programmes to reduce heavy drinking and comorbid depression in Colombia, Mexico, and Peru. The main aims of the evaluation are to assess the implementation of intervention components; mechanisms of impact that influenced the outcomes; and characteristics of the context that influenced implementation and outcomes. Based on this information, common drivers of successful outcomes will be identified. A range of data collection methods will be used: questionnaires; interviews; observations; logbooks; and document analysis. All participating providers will complete a pen-and-paper questionnaire at recruitment and two time points during the implementation period. Providers attending training will complete post-training questionnaires. Additionally, 1080 patients will be invited to self-complete a patient questionnaire. One-in-ten participating providers and fifteen other key stakeholders will participate in semi-structured interviews. Training sessions and community advisory board meetings will be observed by a neutral observer. Logbooks will be kept by local research teams to document events affecting the implementation. Project related documentation and other relevant reports describing the context will be examined.
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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 | 0.108 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.084 | 0.014 |
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".