SickKids Centre for Global Child Health - Chronic Child Malnutrition Project Placement
Bibliographic record
Abstract
I completed a 16-week practicum at the SickKids Centre for Global Child Health where I was a part of the stunting team led by Dr. Nadia Akseer under the research portfolio of Dr. Zulfiqar A. Bhutta. Linear growth stunting, or low height-for-age, is a visible and easily measurable physical manifestation of chronic malnutrition. Children who are stunted have higher rates of mortality and morbidity, as well as experience suboptimal cognitive and motor development. At the time of my placement, one of the team's main projects was a mixed-methods study involving an in-depth evaluation of policies, programs, and factors that have contributed to the decline of under-5 stunting in Ethiopia from 2000-2016. I worked on a variety of components of the manuscript for this study. These included narratives for country demographics, background statistics as well as migration and remittance trends. I also contributed to a literature review on factors that have contributed to a reduction in stunting in Ethiopia in relation to an adapted version of UNICEF's conceptual framework for malnutrition. In addition to the manuscript, I conducted a multivariable analysis of the 2016 determinants of under-5 wasting in Ethiopia. Wasting, or low weight for height, is a form of acute malnutrition and is also a risk factor for mortality. I applied a hierarchical analysis to wasting indicators such as disease, household wealth, maternal education and access to health services. I used Ethiopia's 2016 Demographic and Health Survey data and additional data sources provided through various Ethiopian government ministries. Overall my practicum was a rich interdisciplinary learning experience which allowed me to develop my quantitative and qualitative research skills. I also gained a deeper understanding of global health research processes and the multi-sectoral nature of combating child malnutrition.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
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".