A review of the reliability of remote neuropsychological assessment
Bibliographic record
Abstract
The provision of clinical neuropsychological services has predominately been undertaken by way of standardized administration in a face-to-face setting. Interpretation of psychometric findings in this context is dependent on the use of normative comparison. When the standardization in which such psychometric measures are employed deviates from how they were employed in the context of the development of its associated norms, one is left to question the reliability and hence, validity of any such findings and in turn, diagnostic decision making. In light of the current COVID-19 pandemic and resultant social distancing direction, face-to-face neuropsychological assessment has been challenging to undertake. As such, remote (i.e., virtual) neuropsychological assessment has become an obvious solution. Here, and before the results from remote neuropsychological assessment can be said to stand on firm scientific grounds, it is paramount to ensure that results garnered remotely are reliable and valid. To this end, we undertook a review of the literature and present an overview of the landscape. To date, the literature shows evidence for the reliability of remote administration and the clinical implications are paramount. When and where needed, neuropsychologists, psychometric technicians and examinees may no longer need to be in the same physical space to undergo an assessment. These findings are most relevant given the physical distancing practices because of COVID-19. And whilst remote assessment should never supplant face-to-face neuropsychological assessments, it does serve as a valid alternative when necessary.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".