Normative Data for the Stroop Color Word Test for a North American Population
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment in multiple sclerosis (MS) often involves attentional deficits. The Stroop colour word test, a measure of attention, lacks current normative data for an english-speaking North american MS population. Further some authors suggest the Stroop actually measures processing speed. OBJECTIVE: To generate normative data for the Stroop colour word test that can be used for a Canadian or North american MS population and to examine the relationship between processing speed tests--the Paced auditory Serial addition Test (PASAT) and Symbol Digit Modalities Test (SDMT)--and the Stroop. RESULTS: Data from 146 healthy subjects aged 18-56 was collected. age was significantly although weakly correlated with general intelligence (r=0.168, p=0.043) assessed with the North american adult Reading Test (NAART), and education (r=-0.313, p<0.001). No demographic variables were associated with SDMT or PASAT. age had a low-moderate negative correlation (r=-0.403, p<0.001) with Stroop scores. The mean (±standard deviation, SD) Stroop score was 45.4(10.4). The z-score can thus be calculated as [(X-45.4)/10.4]. if adjusted for age, Xadj = [X-(-0.47)(age-37.5)] and is substituted for X. in a comparison MS population consisting of 75 randomly selected patients from the MS Cognitive clinic, Stroop and PASAT performance were not related. a relationship existed between Stroop and SDMT scores but only 12.2% of the Stroop score variance was explained by the SDMT. Therefore, the Stroop measures selective attention independently of processing speed. CONCLUSION: This data can be used to determine impaired attention in MS patients. Données normatives pour le test mot-couleur de Stroop chez une population nord-américaine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".