Smart Triage Uganda Health Worker and Caregiver Video Series - English Version
Bibliographic record
Abstract
Objective(s): Smart Triage is a digital platform that enables the triage of sick and critically ill children at health facilities in low resourced settings. Understanding the triage process for parents and caregivers is important as it can improve the experience and interactions they will have at the emergency department. The aim of this project is to provide 3 videos that can be viewed in hospital by parents, caregivers, and health workers to keep them informed about the use of the Smart Triage system within the pediatric emergency department and how this affects hospital process and their experience in the hospital. Data Description: This dataset features 3 videos tailored to the Ugandan context. It includes videos focusing on the following topics: 1) Introduction to Smart Triage for Caregivers 2) Going Home after the Hospital for Caregivers 3) Introduction to Smart Triage for Health Care Workers Videos are available in English and local Ugandan languages of Runyankole and Luganda. This dataset contains the English version. Each video is available with captions or without captions. Limitations: Videos were designed for the Ugandan context and may not be generalizable to other settings. Ethics Declaration: NA Funding Source(s): BC Children's Hospital Foundation; Grand Challenges Canada; Mining4Life; Wellcome.
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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 | 0.005 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.180 | 0.083 |
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".