Tailoring an evidence-based clinical intervention and training package for the treatment and prevention of comorbid heavy drinking and depression in middle-income country settings: the development of the SCALA toolkit in Latin America
Bibliographic record
Abstract
BACKGROUND: Effective interventions exist for heavy drinking and depression but to date there has been limited translation into routine practice in global health systems. This evidence-to-practice gap is particularly evident in low- and middle-income countries. The international SCALA project (Scale-up of Prevention and Management of Alcohol Use Disorders and Comorbid Depression in Latin America) sought to test the impact of multilevel implementation strategies on rates of primary health care-based measurement of alcohol consumption and identification of depression in Colombia, Mexico, and Peru. OBJECTIVE: To describe the process of development and cultural adaptation of the clinical intervention and training package. METHODS: We drew on Barrero and Castro's four-stage cultural adaption model: 1) information gathering, 2) preliminary adaption, 3) preliminary adaption tests, and 4) adaption refinement. The Tailored Implementation in Chronic Diseases checklist helped us identify potential factors that could affect implementation, with local stakeholder groups established to support the tailoring process, as per the Institute for Healthcare Improvement's Going to Scale Framework. RESULTS: In Stage 1, international best practice guidelines for preventing heavy drinking and depression, and intelligence on the local implementation context, were synthesised to provide an outline clinical intervention and training package. In Stage 2, feedback was gathered from local stakeholders and materials refined accordingly. These materials were piloted with local trainers in Stage 3, leading to further refinements including developing additional tools to support delivery in busy primary care settings. Stage 4 comprised further adaptions in response to real-world implementation, a period that coincided with the onset of the COVID-19 pandemic, including translating the intervention and training package for online delivery, and higher priority for depression screening in the clinical pathway. CONCLUSION: Our experience highlights the importance of meaningful engagement with local communities, alongside the need for continuous tailoring and adaptation, and collaborative decision-making.
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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 | 0.042 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".