Validation and Reliability of the Dyslexia Adult Checklist in Screening for Dyslexia
Bibliographic record
Abstract
Dyslexia is a language-based neurobiological and developmental learning disability marked by inaccurate and disfluent word recognition, poor decoding, and difficulty spelling. Individuals can be diagnosed with and experience symptoms of dyslexia throughout their lifespan. Screening tools such as the Dyslexia Adult Checklist allow individuals to self-evaluate common risk factors of dyslexia prior to or in lieu of obtaining costly and timely psychoeducational assessments. Although widely available online, the Dyslexia Adult Checklist has yet to be validated. The purpose of this study was to validate this Checklist in a sample of adults with and without dyslexia using both univariate and multivariate statistical approaches. We hypothesised that the Dyslexia Adult Checklist would accurately distinguish between individuals with a self-reported diagnosis of dyslexia (n = 200) and a control group (n = 200), as measured by total scores on the screening tool. Results from our sample found the Dyslexia Adult Checklist to be valid (Cronbach's α = 0.86), and reliable (sensitivity = 76%-91.5%, specificity = 80%-88%). Compared to the originally proposed cut-off score of 45, given the higher sensitivity rate and negative predictive value, we recommend researchers and clinicians use a cut-off score of 40 to indicate possible mild to severe symptoms of dyslexia when using the Dyslexia Adult Checklist.
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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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".