Development and feasibility testing of a mobile phone application to track children’s developmental progression
Bibliographic record
Abstract
Given that mobile phone usage has increased rapidly throughout the world, one possibility to increase parental involvement in monitoring their children's progression is to train parents or primary caregivers on the use of mobile phone technology to track their children's developmental milestones. The current paper aimed to describe the development of a mobile phone application for use among primary caregivers and establish the feasibility and preliminary impact of caregivers using a mobile phone application to track the progression of their children's development in a context where there is a paucity of similar studies. This study is a substudy that focusses on the intervention group only of a recently completed two-armed quasi-experimental study in an informal settlement in Nairobi. The mobile phone application which consisted of questions on children's developmental progression, as well as stimulation messages, was developed through a step-wise approach. The questions covered five child developmental domains: communication; fine motor; gross motor; personal-social; and, problem-solving. Depending on the response received, the child would be classified as having 'achieved a milestone' or 'milestone not achieved.' If a child had achieved the milestone for a specific age, a caregiver would receive an SMS on how to stimulate the child to achieve the next milestone. Where the milestone was not achieved, the caregiver would get a message to enhance development in the area of delay. Caregivers with children aged between six months and two years were recruited into the study and received questions and messages regarding their children's development (age-specific) on a monthly basis for 12 months. Caregiver adherence to the intervention was above 90% in the first three months of implementation. Thereafter, the response rate fluctuated between 76% and 86% across the subsequent months of the intervention. The high level and fairly stable caregivers' rate of response to the 12 rounds of messaging indicated feasibility of the mobile technology. Further, in the first three months of intervention implementation, the majority of caregivers were able to keep track of how their children attained their developmental milestones. The intervention seems to be scalable, practical and potentially low-cost because of the wide coverage of phones.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".