Should we change targets and methods of early intervention in autism, in favor of a strengths-based education?
Bibliographic record
Abstract
Early intensive behavioral intervention (EIBI) and its recent variant, naturalist developmental behavioral intervention (NDBI) aim to increase socialization and communication, and to decrease repetitive and challenging behaviors in preschool age autistic children. These behaviorist techniques are based on the precocity and intensity of the intervention, face-to-face interaction, errorless learning, and information fragmentation. Once considered to be “scientifically proven”, the efficacy of these approaches has been called into question in the last decade due to poor-quality data, small effects, low cost-efficiency, and the evolution of ethical and societal standards. Grounded on a reappraisal of the genetic and cognitive neuroscience of autism, we question three aspects of EIBI/NDBI: their focus on prerequisites for typical socio-communicative behaviors, their lack of consideration of autistic language development and learning modes, and their negative view of repetitive behaviors and restricted interests. We propose alternative predictions for empirical validation, based on the strengths of prototypical autistic children: (a) their non-verbal intelligence should be normally distributed and within the normal range; (b) improving access to non-communicative verbal and written auditory language material should favor their subsequent speech development and (c) decrease their problematic behavior; (d) lateral tutorship should increase the well-being of children and parents to a greater extent than personalized, face-to-face interventions by professionals; (e) admission to regular, but supervised daycare centers, combined with parental support and on-site crisis solving, represents a more cost-effective educational intervention than EIBI/NDBI.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".