Replicating the Expected and the Unexpected: Neuropsychological and Symptom Profiles in a Neurotypical Romanian-English Bilingual Sample
Bibliographic record
Abstract
Establishing the effect of limited English proficiency (LEP) on cognitive performance within linguistically diverse populations is central to cross-cultural neuropsychological assessments. The present study was designed to replicate previous research on cognitive profiles in Romanian-English bilinguals. Seventy-six participants (54 women, MAge = 23.16, SDAge = 5.91; MEducation = 14.49, SDEducation = 1.57) completed a neuropsychological battery in English. The Digit Span, Animal and Emotion Fluency, and several symptom-report scales were also administered in Romanian. Performance patterns were similar to previous findings: verbal fluency, auditory verbal learning, and picture and speeded color naming were highly sensitive to LEP. In contrast, visuomotor processing speed and mental flexibility were robust to LEP. Participants performed better when ability tests were administered in their native language; there was no difference on symptom inventories. Test performance was related to the degree of LEP, operationalized as performance on the Boston Naming Test-Short Form. Level of verbal mediation and LEP are independent predictors of cognitive performance. Administering tests in the native language may provide a more accurate measure of cognitive functioning in examinees with LEP (especially at the low end of English proficiency). Developing population-specific norms is a necessary safeguard against the multiple confounding factors in the neuropsychological assessment of individuals with LEP.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".