Prevalence of Low Scores in Children and Adolescents on the Test of Verbal Conceptualization and Fluency
Bibliographic record
Abstract
It is important to consider the prevalence of low scores when administering a battery of psychological tests. Understanding the prevalence of low scores is important for minimizing false-positive diagnoses of cognitive deficits in clinical practice. The purpose of this study was to expand the literature on base rates for use in children and adolescents. Participants were 408 healthy children and adolescents (M(age) = 13.1 years, SD = 3.7) and 139 children and adolescents (M(age) = 12.4 years, SD = 3.1) diagnosed with a medical, neurological, or learning condition. All participants were administered the Test of Verbal Conceptualization and Fluency (TVCF; Reynolds & Horton, 2006 ). The clinical sample performed significantly lower compared with the healthy control participants on three of the five TVCF scores. When all scores were considered simultaneously, 38% of healthy children obtained one or more scores below the 16th percentile and 15% had one or more scores in the 5th percentile or lower. By comparison, significantly higher proportions of children in the clinical sample had low scores below each of the five cutoffs (i.e., 63% had one or more test scores below the 16th percentile and 37% had one or more scores in the 5th percentile or lower). Our findings illustrate the importance of considering the prevalence of low TVCF scores in everyday clinical practice with children and adolescents.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".