Enhanced detection of suboptimal effort in psychoeducational assessments for dyslexia
Bibliographic record
Abstract
Objective: Although performance validity tests (PVTs) are routinely administered in neuropsychological evaluations, they are employed less frequently in assessments for specific learning disabilities such as dyslexia, likely due, at least in part, to the limited availability of PVTs to evaluate effort on measures of academic achievement. This is troubling, as previous research suggests that up to 24% of postsecondary students undergoing learning disability assessments produce noncredible test scores indicative of symptom exaggeration or low effort. This paper discusses normative data collected for the revised Dyslexia Assessment of Simulation or Honesty- Revised (DASH-R), a PVT developed specifically to identify symptom exaggeration or magnification during dyslexia testing. Method: We administered the DASH-R to three groups of students: honest responding controls (n = 48), students with documented dyslexia (n = 232), and students coached to simulate dyslexia (n = 42). Students were also administered measures of reading and processing speed. Results: DASH-R scores differentiated simulators from both honest responding controls and those with dyslexia. Further, ROC curve analysis showed that a composite feigning index score derived from the DASH-R could be used diagnostically to detect low effort; an optimal cut score of ≥4 on a seven-variable index yielded high specificity (≥98%) and good sensitivity (71%), with positive predictive accuracy of 86%. Creation of a 9-variable index that included errors on an additional reading test produced improved positive predictive accuracy to 96% while retaining excellent specificity (99%). Conclusions: The DASH-R appears to be a promising disability-specific measure for detecting feigned reading problems in young adults undergoing evaluations for dyslexia.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".