Smart Discharges Uganda Caregiver Counselling Video Series - Lusoga Version
Bibliographic record
Abstract
Objective(s): Smart Discharges is a digital health program that uses individual-level risk prediction and intervention to increase effective health seeking behavior, improve health outcomes, and reduce mortality during the post-discharge period. Health workers aim to mitigate risk by educating caregivers on post-discharge care practices and by scheduling follow-up visits for at-risk children in their communities. Data Description: This dataset features a 7-part caregiver counselling video series tailored to the Ugandan context. It includes an introductory video and seven videos focusing on essential post-discharge care practices: 1) Hygiene 2) Nutrition 3) Breastfeeding 4) Care Seeking 5) Mosquito Net Use 6) Medications 7) Immunizations Videos are available in English and local Ugandan languages of Acholi, Luganda, Lusoga, and Runyankole. This dataset contains the Lusoga version. Limitations: Videos were designed for the Ugandan context and may not be generalizable to other settings. Abbreviations: Village Health Teams (VHT) (i.e. local term for Community Health Worker (CHW)) Ethics Declaration: NA Funding Source(s): BC Children's Hospital Foundation; Grand Challenges Canada; Mining4Life; Thrasher Research Fund;
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.133 | 0.066 |
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".