Identifying the functions of restricted and repetitive behaviours and interests in Autism: A scoping review.
Bibliographic record
Abstract
Restricted and repetitive behaviours and interests (RRBIs) are common and integral to the everyday living of autistic persons. While RRBIs are often seen as negative behaviours to be subdued, they may serve regulatory roles that are central to an autistic way of being. Moreover, RRBIs are often present in early childhood and are associated with developmental benefits. Therefore, the continued presence of RRBIs among autistic persons beyond early childhood suggests that RRBIs may serve adaptive functions. To inform and improve clinical decision-making and gain a deeper understanding of the adaptive roles of RRBIs, a scoping review methodology was used to identify the key functions of RRBIs in autism. Our results resulted in 21 studies from the PsycINFO and Medline databases among the 3162 studies on RRBIs published between 2010 and 2023 retrieved. Four themes of functions were identified and mapped onto the subdomains of RRBIs. Specifically, results indicated that RRBIs (i) help autistic persons regulate sensory experiences, (ii) cope with feelings of anxiety, (iii) introduce certainty to their environment, and (iv) make sense of their world. This paper highlights the constructive characteristics of RRBIs and presents an alternative perspective to understand them and help inform decisions about whether and how RRBIs should be managed. Future research should focus on gathering qualitative insights into RRBIs from the perspectives of autistic persons and developing nuanced approaches to managing RRBIs by considering their functions.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".