The Role of Parenting Interventions in Optimizing School Readiness for Children With Disabilities in Low and Middle Income Settings
Bibliographic record
Abstract
Addressing inequity for children with disabilities is enshrined in the UN Convention on the Rights of the Child, the Convention on the Rights of Persons with Disabilities, and the Sustainable Development Goals (SDGs), which include a range of commitments for the provision of disability-inclusive education and health services. Fulfilment of these pledges is important considering that children with disabilities face greater challenges accessing quality education and appropriate health services, and experience worse learning and health outcomes especially in low- and middle-income countries. Urgent action is needed as children with disabilities are being left behind in the ‘thrive and transform’ agenda. Culturally sensitive parenting initiatives must be geared toward ‘school readiness’ for educational inclusion of young children with disabilities. This necessitates that the community, teachers and parents work together with children towards successful developmental and learning outcomes. We make the case for optimising school readiness for children with disabilities by focusing on disability and its determinants within culturally sensitive parenting interventions, and early child development teaching programmes at schools, to better inform parents and teachers and strengthen education systems towards achieving the full intent of SDG 4.2 and global development goals for all.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".