Study Results of mCARE: Developing, Deploying, and Analysing the End-to-End Results of a Mobile-Based Remote Monitoring Tool for Children with Autism Spectrum Disorder in Bangladesh
Bibliographic record
Abstract
Mental health is one of the most neglected healthcare issues in developing countries. The situation is worse in the Global South due to stigma, superstitions, and many other social-cultural-financial constraints. Lack of mental health professionals and regular monitoring has deprived families raising children with Autism Spectrum Disorder (ASD) of the desired care and support. However, the overwhelming adoption of mobile phones in Bangladesh has created an unprecedented opportunity to overcome these various constraints. To leverage this opportunity, we designed, developed, and evaluated mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method), a mobile application that integrates the Experience Sampling Method (ESM) with the local healthcare practice by putting the caregivers in the loop. mCARE collected behavioral and developmental progress parameter values from the caregivers of 300 children with ASD periodically (daily/ weekly/ biweekly/ monthly) along with sociodemographic data of the family. This paper reports: (a) the context and challenge identification phases that validate the requirement of a mobile based tool; (b) evolution of mCARE following Value Sensitive Design; (c) short term and long-term impact analysis (qualitative and quantitative) of mCARE, and; (d) broader implications of these findings for the HCI scholarship along with the impact of mCARE during COVID-19.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".