A validation of the Dyslexia Adult Screening Test (DAST) in a post‐secondary population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Ontario, Canada, there is a demand for psychometrically robust screening tools capable of efficiently identifying students with specific learning disabilities (SLD), such as dyslexia. The present study investigated the ability of the Dyslexia Adult Screening Test (DAST) to discriminate between 117 post‐secondary students with carefully diagnosed SLDs and 121 comparison students. Results indicated that the DAST correctly identified only 74% of the students with SLDs as ‘highly at risk’ for dyslexia. Although employing the cutoff for ‘mildly at risk’ correctly identified 85% of the students with SLDs, this also increased the percentage of students with no major history of learning problems identified as ‘at risk’ for dyslexia from 16% to 26%. These findings suggest that the DAST in its present form is limited in its ability to screen for SLDs. Implications for future research are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it