Remote cognitive assessment in severe mental illness: a scoping review
Why this work is in the frame
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Bibliographic record
Abstract
Many individuals living with severe mental illness, such as schizophrenia, present cognitive deficits and reasoning biases negatively impacting clinical and functional trajectories. Remote cognitive assessment presents many opportunities for advancing research and treatment but has yet to be widely used in psychiatric populations. We conducted a scoping review of remote cognitive assessment in severe mental illness to provide an overview of available measures and guide best practices. Overall, 34 studies (n = 20,813 clinical participants) were reviewed and remote measures, psychometrics, facilitators, barriers, and future directions were synthesized using a logic model. We identified 82 measures assessing cognition in severe mental illness across 11 cognitive domains and four device platforms. Remote measures were generally comparable to traditional versions, though psychometric properties were infrequently reported. Facilitators included standardized procedures and wider recruitment, whereas barriers included imprecise measure adaptations, technology inaccessibility, low patient engagement, and poor digital literacy. Our review identified several remote cognitive measures in psychiatry across all cognitive domains. However, there is a need for more rigorous validation of these measures and consideration of potentially influential factors, such as sex and gender. We provide recommendations for conducting remote cognitive assessment in psychiatry and fostering high-quality research using digital technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it