Teleneuropsychological Assessment During the COVID-19 Pandemic
Bibliographic record
Abstract
Objective: With the rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), and the subsequent alterations to the delivery of health care, telehealth has become an essential service worldwide. Neuropsychology is similarly attempting to adopt telecommunication to deliver neuropsychological services to clients. The purpose this article is to review the utility and value of teleneuropsychological assessment, discuss practical issues and possible barriers related to its implementation and use, and propose considerations and achievable goals to increase the use, acceptance, and clinical utility of teleneuropsychological evaluations. Method: We reviewed the published literature to extract information about the efficacy and limitations of the methods that are currently used to deliver teleneuropsychological services, as well as current guidelines and ethical principles most salient to teleneuropsychological practice. Conclusions: Current literature suggests that teleneuropsychological assessment is feasible and acceptable in many patient populations. Practitioners wishing to implement teleneuropsychological assessment should consider using secure testing platforms, participate in continuing education on the topic of remote/online evaluation, and become familiar with alternative technologies. We implore clinicians, researchers, and trainees who have successfully integrated teleneuropsychology into their current practice to keep detailed records of their methods and results in hopes of adding this data to a larger data repository or to publish these results to add to the small, but growing, teleneuropsychology literature. Future research should focus on generating new normative datasets for tests administered remotely, which will involve the pooling of data from multiple sources using teleneuropsychological assessment.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".