A quasi-experimental study on exploring the use of mobile phone technology for optimizing, tracking and responding to children's developmental progress in Korogocho, Nairobi, Kenya: study protocol
Bibliographic record
Abstract
Background: The massive use of technology can be leveraged to facilitate access to growth and development programs for children. Existing programs supporting such initiatives for children younger than three years are inadequate and not accessible to most families. In most cases, primary caregivers are unable to identify delayed milestones in their children’s growth and development due to inadequate information. They therefore often report the cases when they have become very severe and difficult to reverse. In order to promote early identification of possible developmental delays, African Population and Health Research Center together with Val Partners will develop, implement and evaluate the use of mobile phone technology to help caregivers track their children's developmental outcomes. Methods: The study will employ a quasi-experimental design and will use a mixed-methods approach combining quantitative and qualitative methodologies. In one arm, 110 caregivers will be trained on the use of a mobile phone application to assess child growth and development. The other arm, with 110 caregivers, will receive standard care provided by community health volunteers. Child developmental outcomes will be assessed in both arms. Feasibility of the intervention will be assessed qualitatively. Performance data will be compared across the two arms using mixed linear models to assess the effect of the intervention on child development. Conclusions: The findings are expected to provide evidence on whether the intervention is feasible and has an effect on child developmental outcomes. The results will inform the scalability and sustainability of the project. Trial Registration: The trial has been registered with the Pan African Clinical Trial Registry (www.pactr.org) database (ID number: PACTR201905787868050).
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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 | 0.033 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".