Blended-eLearning Improves Alcohol Use Care in Kenya: Pragmatic Randomized Control Trial Results and Parallel Qualitative Study Implications
Bibliographic record
Abstract
Alcohol use is the 5th most important risk factor contributing to the global burden of diseases, with stigma and a lack of trained health workers as the main barriers to adequate care. This study assesses the impact of providing blended-eLearning courses teaching the alcohol, smoking, and substance involvement screening test (ASSIST) screening and its linked brief intervention (BI). In public and private facilities, two randomized control trials (RCTs) showed large and similar decreases in alcohol use in those receiving the BI compared to those receiving only the ASSIST feedback. Qualitative findings confirm a meaningful reduction in alcohol consumption; decrease in stigma and significant practice change, suggesting lay health workers and clinicians can learn effective interventions through blended-eLearning; and significantly improve alcohol use care in a low- and middle-income country (LMIC) context. In addition, our study provides insight into why lay health workers feedback led to a similar decrease in alcohol consumption compared to those who also received a BI by clinicians. Supplementary Information: The online version contains supplementary material available at 10.1007/s11469-022-00841-x.
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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 | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".