Bibliographic record
Abstract
Providing treatment to children with autism is a global health priority, and research demonstrates that caregivers can be trained in techniques to promote their child's social interaction, communication, play, positive behavior and skills. These caregiver-mediated interventions have been shown to promote a number of positive outcomes in children with autism, as well as their caregivers. When provided by telehealth, data indicate that caregiver training is acceptable and feasible, and associated with similar positive outcomes as live face-to-face training. Telehealth innovations, which have accelerated during the COVID-19 era, have demonstrated advantages over in-person delivery of services in terms of cost effectiveness and increased accessibility, however, more research is needed on feasibility, acceptability and effectiveness for different populations in different contexts. This brief review will highlight recent caregiver skills training interventions for autism that have been successfully adapted or designed for telehealth delivery. Telehealth interventions that are scalable, adaptable, caregiver-mediated, open-access, and delivered as part of a stepped care model, have the potential to address the global treatment gap for families of children with autism and other neurodevelopmental disabilities. Considerations relevant to the global scale-up of caregiver-mediated interventions will also be discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".