Blended-eLearning Impact on Health Worker Stigma Toward Alcohol, Tobacco, and Other Psychoactive Substance Users
Bibliographic record
Abstract
Abstract This study evaluated factors affecting the completion of blended-eLearning courses for health workers and their effect on stigma. The two courses covered the screening and management of harmful alcohol, tobacco, and other substance consumption in a lower-middle-income country setting. The courses included reading, self-reflection exercises, and skills practice on communication and stigma. The Anti-Stigma Intervention-Stigma Evaluation Survey was modified to measure stigma related to alcohol, tobacco, or other substances. Changes in stigma score pre- and post-training period were assessed using pairedt-tests. Of the 123 health workers who registered, 99 completed the pre- and post-training surveys, including 56 who completed the course and 43 who did not. Stigma levels decreased significantly after the training period, especially for those who completed the courses. These findings indicate that blended-eLearning courses can contribute to stigma reduction and are an effective way to deliver continuing education, including in a lower-middle-income country setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".