The license plate test performance in Canadian adolescents with learning disabilities: A preliminary study
Bibliographic record
Abstract
Accurate identification of symptom exaggeration is essential when determining whether data obtained in pediatric evaluations are valid or interpretable. Performance validity measures identify performance patterns that are implausible if the test taker is investing full effort; however, it is unclear whether or not persons with preexisting cognitive difficulties such as Specific Learning Disabilities (SLD) might be falsely accused of poor test motivation due to actual but impaired reading, processing or memory skills. The purpose of this study was to evaluate the newly developed License Plate Test (LPT) performance in students with identified SLD providing good effort, to examine the influence of severe reading or learning problems on LPT performance. Participants were 29 students with SLDs aged 11–14 years (M = 12.1), who completed psycho-educational assessments as part of a transition program to secondary school. Results indicate that recognition memory measures on the LPT were insensitive to cognitive impairments in these children; all students achieved scores of 80% or higher on these tasks. Performance was more variable as test demands of the LPT increased, and the difference between performance on easy and hard subtests was related to greater difficulties with working memory. These results provide preliminary data regarding how children with SLD perform on the LPT, allowing for development of appropriate cut scores to maximize sensitivity and specificity of this test for use with child and adolescent populations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".