Studying the Effectiveness of Video‐Assisted Education in Teaching Ugandan Mothers to Monitor Their Hospitalised Infant
Bibliographic record
Abstract
AIM: To determine the time required for mothers to learn how to monitor their hospitalised infant using video-assisted education and to understand healthcare providers' perspectives on their role in maternal education. METHODS: This was a two-part study in a neonatal hospital unit in Uganda. Using video-assisted education, we assessed the time required for mothers to learn how to assess danger signs, weigh their baby, and track feeds. Proficiency was tested with knowledge and skills assessments. Concurrently, healthcare providers completed a survey regarding maternal engagement. RESULTS: Fifty mothers participated, and 72% watched the training video only once. After viewing the video, 36% needed no additional nursing time to achieve proficiency. For mothers requiring nursing assistance, nurses spent a median time of 37 s (IQR 21-78 s). Ninety-four percent and 72% of mothers had perfect scores on the knowledge and skills assessment, respectively. Twelve healthcare providers were surveyed, and 58% reported having < 10 min to spend on maternal education. All providers cited recognition of danger signs as the most important skill for mothers to learn. CONCLUSION: Given Uganda's healthcare provider shortage, video-assisted education may be a valuable tool to teach mothers how they can participate in their hospitalised infant's care.
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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 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".