What do autistic children who are interested in letters and numbers do with them? A qualitative study
Bibliographic record
Abstract
PURPOSE: Over a third of autistic children exhibit an intense or exclusive interest in letters and numbers at the time of diagnosis. This article aims to qualitatively investigate the atypical manifestations of this interest in autism compared to typically developing children and determine if and how it can benefit children and their families. METHODS: The participants were the parents of 138 autistic children (84% were non-speaking or minimally speaking) and 76 typically developing children ages 2-6. They were administered a questionnaire on their child's interest in letters and numbers, the manifestations of these interests, the parental attitude towards it, and the child's oral language. An inductive thematic analysis was performed on the qualitative data to establish recurring themes. RESULTS: Eight themes were identified: atypical behaviours related to written material, emotional attachment to letters and numbers, language acquisition, use of screens, solitary behaviour, reduction of the interest over time, parental attitudes, and other special abilities. CONCLUSION: This study reveals that the interest in written material manifests itself in atypical ways in autism and is not comparable to the development of an interest in reading in a typically developing context. This interest also presents multiple beneficial aspects for children and their families.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".