Trends in the neuropsychological assessment of ethnic/racial minorities: A survey of clinical neuropsychologists in the United States and Canada.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".