Evaluating the use of educational videos to support the tuberculosis care cascade in remote Madagascar
Bibliographic record
Abstract
SETTING: Access to information about tuberculosis (TB) is vital to ensure timely diagnosis, treatment, and control among vulnerable communities. Improved approaches for distributing health education materials to remote populations are needed. OBJECTIVE: To evaluate the impact of two comprehensive video training curricula in improving patient, community member, and community health worker knowledge of TB in a remote area of Madagascar. DESIGN: A pre-test/post-test design was used to measure knowledge acquisition. Educational videos were short, culturally appropriate films presented at critical moments in the TB cascade of care. RESULTS: Of the total 146 participants, 86 (58.9%) improved their score on the post-test, 50 (34.2%) obtained the same score, and 10 (6.8%) received a worse score. A statistically significant difference was observed between the pre- and post-test scores, wherein scores increased by a median of 10.0% (interquartile range 0.0–20.0) after viewing the videos ( P < 0.001). There was a significant difference between the number of correct answers on the pre-test and the number of correct answers on the post-test ( P < 0.001). CONCLUSION: Educational videos were found to significantly improve TB knowledge among a low-literacy, remote population in Madagascar. Our findings suggest educational videos could be a powerful, low-cost, and sustainable tool to improve access to TB education materials globally.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".