Impaired or Not Impaired, That Is the Question: Navigating the Challenges Associated with Using Canadian Normative Data in a Comprehensive Test Battery That Contains American Tests
Bibliographic record
Abstract
It has been well documented that IQ scores calculated using Canadian norms are generally 2-5 points lower than those calculated using American norms on the Wechsler IQ scales. However, recent findings have demonstrated that the difference may be significantly larger for individuals with certain demographic characteristics, and this has prompted discussion about the appropriateness of using the Canadian normative system with a clinical population in Canada. This study compared the interpretive effects of applying the American and Canadian normative systems in a clinical sample. We used a multivariate analysis of variance (ANOVA) to calculate differences between IQ and Index scores in a clinical sample, and mixed model ANOVAs to assess the pattern of differences across age and ability level. As expected, Full Scale IQ scores calculated using Canadian norms were systematically lower than those calculated using American norms, but differences were significantly larger for individuals classified as having extremely low or borderline intellectual functioning when compared with those who scored in the average range. Implications of clinically different conclusions for up to 52.8% of patients based on these discrepancies highlight a unique dilemma facing Canadian clinicians, and underscore the need for caution when choosing a normative system with which to interpret WAIS-IV results in the context of a neuropsychological test battery in Canada. Based on these findings, we offer guidelines for best practice for Canadian clinicians when interpreting data from neuropsychological test batteries that include different normative systems, and suggestions to assist with future test development.
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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 | 0.070 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".