A global health project: creating sustainable solutions to address anemia at Munsel-ling school in rural northern India
Bibliographic record
Abstract
Anemia is a major public health concern in India, especially with- in the rural population. Six years ago, a group of medical students from the University of British Columbia began a collaboration with a boarding school in the Spiti Valley area of Northern India. The team found a high prevalence of anemia within the school population and devised a set of sustainable proj- ects to improve student health, includ- ing health education, greenhouses, water and sanitation, and iron supplementation. Health screens were also integrated ev- ery year to track changes in the popula- tion's health over time. To date, these in- terventions have significantly decreased the students' levels of anemia over the five-year period. However, the most effec - tive intervention appears to be direct iron supplementation, yet the sustainability of this practice remains challenging. Global Health Initiative (GHI) program, the Spiti Project. This project estab- lished a partnership with the Munsel-ling Boarding School in the village of Rangrik and its affiliated local Non-Government Organization (NGO), Rinchen Zangpo Society for Spiti Development. The school is privately run and is currently respon- sible for the education of approximately 700 children from surrounding commu- nities, housing three quarters of the stu- dents for the entire school year. Since its inauguration, the Spiti Project has also collaborated with the Vancouver-based Trans-Himalayan Aid Society (TRAS) and several other NGOs for funding. In 2006, the first UBC Spiti Valley team
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".