Annual Research Review: Achieving universal health coverage for young children with autism spectrum disorder in low‐ and middle‐income countries: a review of reviews
Bibliographic record
Abstract
Background Autism presents with similar prevalence and core impairments in diverse populations. We conducted a scoping review of reviews to determine key barriers and innovative strategies which can contribute to attaining universal health coverage (UHC), from early detection to effective interventions for autism in low‐ and middle‐income countries (LAMIC). Methods A systematic literature search of review articles was conducted. Reviews relevant to the study research question were included if they incorporated papers from LAMIC and focused on children ( Results We identified 31 articles which reviewed data from over fifty countries across Africa, Latin America, Middle East, and Asia and addressed barriers across one or more of four inter‐related domains: (a) the social context and family experience for a child with autism; (b) barriers to detection and diagnosis; (c) access to appropriate evidence‐based intervention; and (d) social policy and legislation. Key barriers identified included: lack of appropriate tools for detection and diagnosis; low awareness and experienced stigma impacting demand for autism care; and the prevalence of specialist models for diagnosis and treatment which are not scalable in LAMIC. Conclusions We present a Theory of Change model which describe the strategies and resources needed to realize UHC for children with autism in LAMIC. We highlight the importance of harnessing existing evidence to best effect, using task sharing and adapted intervention strategies, community participation, and technology innovation. Scaling up these innovations will require open access to appropriate detection and intervention tools, systematic approaches to building and sustaining skills in frontline providers to support detection and deliver interventions embedded within a stepped care architecture, and community awareness of child development milestones.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".