Focus group reflections on the current and future state of cognitive assessment tools in geriatric health care
Bibliographic record
Abstract
OBJECTIVE: This study provides insight into the thoughts and opinions of geriatric health-care professionals toward cognitive assessments and the use of emerging technologies, such as eye-tracking, to supplement current tools. METHODS: Two focus group sessions were conducted with nurses and physicians who routinely administer neurocognitive assessments to geriatric populations. Video recordings of the focus group sessions were transcribed and a thematic analysis was performed. RESULTS: Participants reported the need for assessment and diagnostic tools that are accessible and efficient, and that are capable of accommodating the rapid growth in the aging population. The prevalence of more complex ailments experienced by older adults has had repercussions in the quality of care that the clients receive, and has contributed to lengthy wait times and resource shortages. Health-care professionals stated that they are hampered by the disjointed structure of the health-care system and that they would benefit from a more efficient allocation of responsibilities made possible through tools that did not require extensive training or certification. Eyetracking-based cognitive assessments were thought to strongly complement this system, yet it was thought that difficulty would be faced in gaining the support and increased uptake by health-care professionals due to the nonintuitive relationship between eyetracking and cognition. CONCLUSION: The findings suggest that health-care professionals are receptive to the use of eyetracking technology to assess for cognitive health as it would conserve resources by allowing frontline staff to administer assessments with minimal training.
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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 | 0.069 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| 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; 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".