Patient perspectives of the experience of a computerized cognitive assessment in a clinical setting
Bibliographic record
Abstract
INTRODUCTION: Computerized assessments are becoming widely accepted in the clinical setting and as a potential outcome measure in clinical trials. To gain patient perspectives of this experience, the aim of the present study was to investigate patient attitudes and perceptions of the Cognigram [Cogstate], a computerized cognitive assessment. METHODS: Semi-structured interviews were conducted with 19 older adults undergoing a computerized cognitive assessment at the University of British Columbia Hospital Clinic for Alzheimer Disease and Related Disorders. Thematic analysis was applied to identify key themes and relationships within the data. RESULTS: The analysis resulted in three categories: attitudes toward computers in healthcare, the cognitive assessment process, and evaluation of the computerized assessment experience. The results show shared views on the need for balance between human and computer intervention, as well as room for improvement in test design and utility. DISCUSSION: Careful design and user-testing should be made a priority in the development of computerized assessment interfaces, as well as reevaluating the cognitive assessment process to minimize patient anxiety and discomfort. Future research should move toward continuous data capture within clinical trials and ensuring instruments of high reliability to reduce variance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".