Surviving and Thriving
Bibliographic record
Abstract
Global attention on early child development, inclusive of those with disability, has the potential to translate into improved action for the millions of children with developmental disability living in low- and middle-income countries. Nurturing care is crucial for all children, arguably even more so for children with developmental disability. A high proportion of survivors of neonatal conditions such as prematurity and neonatal encephalopathy are affected by early child developmental disability. The first thousand days of life is a critical period for neuroplasticity and an important window of opportunity for interventions, which maximize developmental potential and other outcomes. Since 2010, our group has been examining predictors, outcomes, and experiences of neonatal encephalopathy in Uganda. The need for an early child intervention program to maximize participation and improve the quality of life for children and families became apparent. In response, the "ABAaNA early intervention program," (now re-branding as 'Baby Ubuntu') a group participatory early intervention program for young children with developmental disability and their families, was developed and piloted. Piloting has provided early evidence of feasibility, acceptability, and impact and a feasibility trial is underway. Future research aims to develop programmatic capacity across diverse settings and evaluate its impact at scale.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.014 |
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".