Perceived appropriateness of alcohol screening and brief advice programmes in Colombia, Mexico and Peru and barriers to their implementation in primary health care – a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Providing alcohol screening and brief advice (SBA) in primary health care (PHC) can be an effective measure to reduce alcohol consumption. To aid successful implementation in an upper middle-income country context, this study investigates the perceived appropriateness of the programme and the perceived barriers to its implementation in PHC settings in three Latin American countries: Colombia, Mexico and Peru, as part of larger implementation study (SCALA). METHODS: An online survey based on the Tailored Implementation for Chronic Diseases (TICD) implementation framework was disseminated in the three countries to key stakeholders with experience in the topic and/or setting (both health professionals and other roles, for example regional health administrators and national experts). In total, 55 respondents participated (66% response rate). For responses to both appropriateness and barriers questions, frequencies were computed, and country comparisons were made using Chi square and Kruskal-Wallis non-parametric tests. RESULTS: Alcohol SBA was seen as an appropriate programme to reduce heavy alcohol use in PHC and a range of providers were considered suitable for its delivery, such as general practitioners, nurses, psychologists and social workers. Contextual factors such as patients' normalised perception of their heavy drinking, lack of on-going support for providers, difficulty of accessing referral services and lenient alcohol control laws were the highest rated barriers. Country differences were found for two barriers: Peruvian respondents rated SBA guidelines as less clear than Mexican (Mann-Whitney U = -18.10, P = 0.001), and more strongly indicated lack of available screening instruments than Colombian (Mann-Whitney U = -12.82, P = 0.035) and Mexican respondents (Mann-Whitney U = -13.56, P = 0.018). CONCLUSIONS: The study shows the need to address contextual factors for successful implementation of SBA in practice. General congruence between the countries suggests that similar approaches can be used to encourage widespread implementation of SBA in all three studied countries, with minor tailoring based on the few country-specific barriers.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".