Parent peer coaching program: A cascading intervention for parents of children with autism in Mongolia
Bibliographic record
Abstract
Parents of children with autism in low-resource settings have reported exacerbated difficulties related to raising their children. In this single-case research using multiple probe design, four parent mentors and five parent peers and their children with autism in Mongolia participated in the parent peer coaching program. The intervention package, including training and coaching in evidence-based practices, was delivered via telepractice. Parent mentors completed online training and were coached by a bilingual and bicultural research assistant in a staggered fashion. Visual analysis revealed a functional relation between the intervention package and the coaching fidelity of parent mentors. Social validity data indicated that all participants were satisfied with the program and reported it was acceptable, feasible, and effective. Implications for conducting intervention research in a low-resource setting are described. Lay abstract Parents of children with autism are known to experience severe hardships related to raising their children. These hardships are exacerbated in low-resource settings internationally where there is very little resource for children and their families, including professionals who provide evidence-based treatment. Mongolia was chosen as an example of such low-resource settings in this single-case research, and four parent mentors and five parent peers and their children with autism participated and completed the study. A local parent group, the Autism Association of Mongolia, was actively involved in this study and helped with recruitment, development, adaptation, and implementation of the intervention to increase acceptability and feasibility. In addition, a local bilingual research assistant was also utilized as the purpose of this study was to build capacity of diverse stakeholders of children with autism in Mongolia. The research assistant was trained and coached by the research team on both content (communication teaching strategies and behavior management) and delivery (coaching adults), who then provided coaching to parent mentors via live videoconferencing in Mongolian. Parent mentors then similarly provided coaching to parent peers after observing the interactions with their children with autism. The findings suggest that parents can effectively deliver high-fidelity coaching to disseminate evidence-based treatment in low-resource settings when given proper training and coaching. Further examination on scalability and sustainment of effects is suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".