Contributing Towards a Cultural Neuropsychology Assessment Decision-Making Framework: Comparison of WAIS-IV Norms from Colombia, Chile, Mexico, Spain, United States, and Canada
Bibliographic record
Abstract
OBJECTIVE: Test and normative data selection in cross-cultural neuropsychology remain a complex issue. Despite growing awareness, more studies and instruments are needed to adequately address the impact of cultural factors, such as quantity and quality of education. In this study, we examine the interpretive effects of applying six relevant WAIS-IV norms to a Colombian sample. METHOD: A sample of 305 highly educated Colombian corporate executives completed the WAIS-IV. Data were scored using norms from Colombia, Chile, Mexico, Spain, United States, and Canada and scores were compared using ANOVA. Additionally, a comparative sociodemographic framework was established to contextualize our sample to the standardization samples and populations of the six countries. RESULTS: Colombian and Chilean norms yielded systematically similar FSIQ/Index scores (mean range = 117-121), while incrementally lower scores were found with norms from Mexico (-3-9 points), Spain (-3-11 points), USA (-8-13 points), and Canada (-11-18 points). Verbal scores differed, with highest scores obtained with Mexican and Spanish norms. Working memory and processing speed scores had the lowest score agreement across norms. CONCLUSIONS: Although the Chilean norms are more frequently used in Colombia, the recently developed Colombian norms appear optimal for our sample; the scores do not have meaningful differences with those obtained with Chilean norms and offer local population representation fidelity. Mexican, Spanish, US, and Canadian norms underestimated WAIS-IV scores and distorted the sample's score distribution. Finally, verbal scores highlight potential education representation within Spanish and Mexican norms, while working memory and processing speed scores suggest cultural nuances likely captured within different norms.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".