Adapting Very Brief Advice (VBA) on smoking for use in low-resource settings: experience from the FRESH AIR project
Bibliographic record
Abstract
Abstract Introduction Very Brief Advice (VBA) on smoking is an evidence-based intervention and a recommended clinical practice for all healthcare professionals in the UK. Aims We report on experience from the FRESH AIR project in adapting the VBA model and training in three low-resource settings: Greece, Vietnam and Kyrgyzstan. Methods Using a participatory research process, UK experts and local stakeholders conducted an environmental scan and needs assessment to examine the VBA intervention model, training materials and recommend adaptations to the local context. Two VBA training sessions were piloted in each country to inform adaptation. A final training tool kit was developed in the local language. Results In each country, the VBA on smoking intervention model remained primarily intact. The lack of a formal smoking cessation system to refer motivated clients in two countries required adaptation of the ACT component of the model. A range of local adaptations to the training resources were made in all three countries to ensure cultural appropriateness as well as enhance key messages including expanding training on nicotine addiction, second-hand smoke and pharmacotherapy. Conclusions Implementation of VBA requires sensitive, collaborative, local and cultural adaptation if it is to be achieved successfully. Trial registration Trial ID# NTR5759 Critical appraisal tools The Standards for Reporting Implementation Studies (StaRI) statement: https://www.equator-network.org/reporting-guidelines/stari-statement/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".