Initiation of Development of an Early Warning System to Locate "Pockets of Child Undernutrition" at District Level
Bibliographic record
Abstract
Importance of an early warning system capable of detecting most needy populations beforehand is emphasized in preparation for effective response, particularly for a developing country like Sri Lanka where most nutrition interventions are established and maintained with limited resources. Therefore, the objective of the current study was to develop an Early Warning System to identify early “pockets of child undernutrition” by Medical Officer of Health/Deputy Director of Health Services (MOH/DDHS) divisions in Kandy District. Prevalence of underweight among children aged 1-5 years was the indicator used. MOH/DDHS areas where child underweight prevalence was continuous at least for eight quarters exceeding 30% were classified as “pockets of child undernutrition”. Predicted under 5 year old child underweight prevalence (determined using secondary data collected from year 2003 to 2006) from first quarter of 2007 to third quarter of 2009 in Kandy District, were cross-validated with real time data. Using the same trend analysis model, child underweight status for fourth quarter of 2009 and first quarter of 2010 in Kandy District (MOH/DDHS area wise) were predicted and mapped using Arc View (version 3.2) software. Predictions were significantly validated with real time data (p<0.05). As per the developed early warning system, Hasalaka and Medadumbara MOH/DDHS areas were the real “pockets” that should be mostly targeted in future interventions. Further, possibilities to improve and enhance the quality of suggested early warning system were also investigated. <strong>Keywords: </strong>Child undernutrition; early warning system; predictions. DOI: <a href="http://dx.doi.org/10.4038/tar.v22i3.3703">http://dx.doi.org/10.4038/tar.v22i3.3703</a> <em>Tropical Agricultural Research </em>22(3) (2011) 305-313
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".