Beyond intervention into daily life: A systematic review of generalisation following social communication interventions for young children with autism
Bibliographic record
Abstract
Researchers have generally considered autistic individuals to have difficulties generalising learned skills across novel contexts. Successful generalisation is necessary for an intervention to have benefits in everyday life beyond the original learning environment. We conducted a systematic review of randomised controlled trials of early social communication interventions for children with autism in order to explore generalisation and its measurement. We identified nine RCTs that provided evidence of initial target learning and measured generalisation, of which eight demonstrated at least some successful generalisation across people, settings, and/or activities. The findings did not support the widely reported generalisation 'difficulties' associated with autism. However, generalisation was not consistent across all skills within studies, and one study found no generalisation despite evidence for initial target learning within the intervention context. In general, there are few methodologically sound social communication intervention studies exploring generalisation in autism and no consensus on how it should be measured. In particular, failure to demonstrate initial learning of target skills within the intervention setting and an absence of formal mediation analyses of the hypothesised mechanisms limit current research. We outline a framework within which measurement of generalisation can be considered for use in future trials. To maximise the effectiveness of interventions, the field needs to gain a better understanding of the nature of generalisation among autistic individuals and what additional strategies may further enhance learning. Autism Res 2020, 13: 506-522. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. LAY SUMMARY: It is generally considered that autistic individuals experience difficulties applying things they have learned in one context into different settings (e.g. from school to home). This is important to consider for intervention studies. Our review does not support a complete lack of generalisation but instead suggests that after early social communication intervention, autistic children can transfer some skills to new contexts. Overall, there is limited research in this area and further work is needed.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".