ABRACADABRA literacy instruction delivered by speech-language pathologists to children with autism during the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: This feasibility study explored literacy instruction for children with autism in an area of socioeconomic disadvantage during the COVID-19 pandemic. METHOD: Fifty-nine autistic children (5-12 years) participated in a baseline assessment before being assigned to one of two instruction conditions or a control group. The first instruction group participants received a total of 13 weeks of literacy instruction using ABRACADABRA (a free online web application), delivered by a speech-language pathologist. The second instruction condition also received a total 13 weeks of ABRACADABRA literacy instruction, supplemented with shared book reading. The control group continued their business-as-usual school and other activities over the 13 weeks. RESULT: Children who participated in instruction made statistically significant gains in their nonword reading skills from pre- to post-instruction with a large effect size. There were no other statistically significant results at the conservative alpha level utilised. However, effect sizes for all reading outcome measures were similar to previous research using ABRACADABRA with autistic children (with medium to large effect sizes observed across various reading accuracy and reading comprehension skills). CONCLUSION: Further research on literacy instruction delivered via via in person sessions and telepractice for children with autism is greatly needed. These findings contribute to the scarce knowledge base of literacy instruction for children with autism and the impact of the COVID-19 pandemic on this group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".