Does Size Really Matter? Contributions to the Debate on Short Versus Long Neuropsychology Assessments
Bibliographic record
Abstract
ObjectiveThere has been increasing interest in recent years in the variation in assessment practices within the neuropsychology profession. This article explores one of the central areas of variation by reviewing the issues surrounding brief versus more comprehensive assessments and some of the advantages and disadvantages of the two approaches.MethodsSome of the many factors influencing the length of assessments that neuropsychologists choose to conduct, and the way these are interpreted, are discussed. These factors include the principles of test selection, the potential of measurement error, the emphasis we place on our previous experience to guide selection and interpretation of tests, and our ethical and legal obligations. The potential utility of employing testing assistants to perform the routine parts of assessments is also explored.ResultsWhile there can be some disadvantages to conducting comprehensive assessments, many benefits of this approach are also identified.ConclusionsOverall, it is argued that neuropsychologists should abide by evidence‐based practices that stem from scientific theory as opposed to conducting less reliable assessments that may be largely driven by cost‐effectiveness.
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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 | 0.195 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".