Trends in the neuropsychological assessment of ethnic/racial minorities: A survey of clinical neuropsychologists in the United States and Canada.
Why this work is in the frame
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Bibliographic record
Abstract
Despite the importance of diversity variables to the clinical practice of neuropsychology, little is known about neuropsychologists' multicultural assessment practices and perspectives. The current study was the first to survey issues related to neuropsychologists' assessment of minority populations, proficiency in languages other than English, approaches to interpreting the cognitive scores of minorities, and perceived challenges associated with assessing ethnic/racial minority patients. We also surveyed respondents with regard to their own demographic backgrounds, as neuropsychologists who identify as ethnic/racial minorities are reportedly underrepresented in the field. Respondents were 512 (26% usable response rate; 54% female) doctorate-level psychologists affiliated with the International Neuropsychology Society or the National Academy of Neuropsychology who resided in the United States or Canada. Overall, results suggest that lack of appropriate norms, tests, and referral sources are perceived as the greatest challenges associated with assessment of ethnic/racial minorities, that multicultural training is not occurring for some practitioners, and that some are conducting assessments in foreign languages despite limited proficiency. In addition, ethnic/racial minorities appear to be grossly underrepresented in the field of neuropsychology. Findings are discussed in relation to the need for appropriate education and training of neuropsychologists in multicultural issues and the provision of more valid assessments for ethnic/racial minority individuals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it