Transporting and implementing a caregiver-mediated intervention for toddlers with autism in Goa, India: evidence from the social ABCs
Bibliographic record
Abstract
Introduction Autism is a global health priority with an urgent need for evidence-based, resource-efficient, scalable supports that are feasible for implementation in low- and middle-income countries (LMICs). Initiating supports in the toddler years has potential to significantly impact child and family outcomes. The current paper describes the feasibility and outcomes associated with a Canadian-developed caregiver-mediated intervention for toddlers (the Social ABCs), delivered through a clinical service in Goa, India. Methods Clinical staff at the Sethu Centre for Child Development and Family Guidance in Goa, India, were trained by the Canadian program development team and delivered the program to families seen through their clinic. Using a retrospective chart review, we gathered information about participating families and used a pre-post design to examine change over time. Results Sixty-four families were enrolled (toddler mean age = 28.5 months; range: 19–35), of whom 55 (85.94%) completed the program. Video-coded data revealed that parents learned the strategies (implementation fidelity increased from M = 45.42% to 76.77%, p < .001, with over 90% of caregivers attaining at least 70% fidelity). Toddler responsivity to their caregivers (M = 7.00% vs. 46.58%) and initiations per minute (M = 1.16 vs. 3.49) increased significantly, p's < .001. Parents also reported significant improvements in child behaviour/skills (p < .001), and a non-significant trend toward reduced parenting stress (p = .056). Discussion Findings corroborate the emerging evidence supporting the use of caregiver-mediated models in LMICs, adding evidence that such supports can be provided in the very early years (i.e., under three years of age) when learning may be optimized.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".