Starting with small health data opportunities for Mhealth in Africa
Bibliographic record
Abstract
The need to obtain data to understand effective and available child mortality-reducing control measures in rural areas of developing countries is great.Evidence shows that this challenge can potentially be overcome with the increased availability of Information and Communication Technology (ICT) to support the data/information/ knowledge needs of healthcare delivery services in low resource settings.Recognising the benefits of ICT and the need for improvements in the Nigerian health sector, this paper outlines the plans for the technical feasibility assessment of the IMPACT (usIng Mobile Phones for Assessing, Classifying and Treating sick children) smartphone application to capture, store and analyse of child health assessment data.IMPACT is a secure, scalable, user friendly mobile health (mHealth) innovation that is being developed to support 'small data' capabilities within the context of healthcare in the community in Enugu State, Nigeria, Africa.Notwithstanding the heightened focus on 'big data' in health, this research is interested in investigating the opportunities associated with doing 'small healthcare data' well, with the long term view of building to the big data scenario for healthcare in the community in Enugu.This paper outlines the plan for the IMPACT project considering the implications for health data, knowledge management in healthcare and the big data opportunities to support disease surveillance, healthcare delivery and resourcing and healthcare practitioner education.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".