Comparative Effectiveness of Human- and Robot-Based Interventions in Increasing Empathy Among Autistic Children
Bibliographic record
Abstract
METHODS: A total of 82 Chinese-speaking children with autism aged 4-9 years were assigned to an HBI or an RBI group through stratified randomization. Before the intervention, the children's autism severity, verbal comprehension, and Theory of Mind skills were assessed. Each child received four 30-min training sessions over 4 weeks, during which they watched four dramas performed by human or robot actors, in which one character shared an event with another character, who then displayed an empathic response. Parents completed a questionnaire before, immediately after, and 1 month after the intervention, and children's cognitive empathy (CE), affective empathy (AE), and prosocial behavior (PB) were evaluated in an experimental task. RESULTS: Both RBI and HBI training promote empathy skills, specifically CE, AE, and PB, as evaluated through children's verbal responses in story tasks. CONCLUSION: RBI empathy training demonstrates comparable reliability and effectiveness to human teaching, suggesting that RBIs can assist human therapists in promoting empathy in children with autism. Our randomized controlled trial was registered in the Chinese Clinical Trial Registry (no. ChiCTR2300077745, https://www.chictr.org.cn ).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".