Stability in Test-Usage Practices of Clinical Neuropsychologists in the United States and Canada Over a 10-Year Period: A Follow-Up Survey of INS and NAN Members
Bibliographic record
Abstract
As a 10-year follow up to our original study (Rabin, Barr, & Burton, 2005), we surveyed the test usage patterns of clinical neuropsychologists in the U.S and Canada. We expanded the original questionnaire to include additional cognitive and functional domains and to address current practice-related issues. Participants were randomly selected from the combined membership lists of the National Academy of Neuropsychology and the International Neuropsychological Society. Respondents were 512 doctorate-level members (25% usable response rate; 54% women) who had been practicing neuropsychology for 15 years on average. The Wechsler Adult Intelligence Scales, followed by the Wechsler Memory Scales, Trail Making Test, California Verbal Learning Test, and Wechsler Intelligence Scale for Children, were the most commonly used tests. These top five responses were identical and in the same order as those from 10 years ago. Participants respectively identified a lack of ecological validity and difficulty comparing the meaning of standardized scores across tests as the greatest challenges associated with the selection of neuropsychological instruments and interpretation of test data. Overall, we found great consistency in assessment practices over the 10-year period. We compare results to those of previous studies and discuss challenges and implications for neuropsychology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".