Applying UCD and ZET to Develop a Cloud-Based Real Time Solution for Air Quality Monitoring and Its Effects on Child and Maternal Health in Mongolia
Bibliographic record
Abstract
Air pollution is responsible for 4.2 million premature deaths every year. Studies have proven that Ulaanbaatar, the capital city of Mongolia, is one of more polluted cities in the world. As a result, Mongolia is suffering from major public health challenges. Mongolia currently lacks quality data, evidence and information to analyze and understand the full impact of air pollution on maternal and child health. This lack of understand has led to Family Health Centres (FHCs) and hospitals in Mongolia to be overwhelmed and unprepared to adequately treat air pollution related diseases. In response to this problem, UNICEF Mongolia and Ubilab will use User-Centered Design (UCD) and Zero-effort technology to create an online platform that will use predictive analytics to strengthen the understanding of the impact air pollution has on maternal and child health. This platform will better prepare healthcare practitioners to deal with the public health consequences associated with air pollution and the data generated from this platform will be used to inform policy, health care reforms, and develop educational materials. This study is a great opportunity to demonstrate how UCD and ZET can be effective to achieve goals within a global health perspective, but it would be challenging to overcome the economic and cultural barriers in the design and implementation process. However, if successful, this would enhance collaboration between environment and health-related institutions and can be implemented anywhere in the world, especially in areas where air pollution is a major problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".