Integrating a Standardized Self-Report Tool into Geriatric Medicine Practice during the COVID-19 Pandemic: A Mixed-Methods Study
Bibliographic record
Abstract
Specialized geriatric services care for older adults (≥ 65 years of age) with dementia and other progressive neurological disorders, frailty, and mental health conditions were provided both virtually and in person during the pandemic. The objective of this study was to implement a software-enabled standardized self-report instrument - the interRAI Check-Up Self-Report - to remotely assess patients. A convergent, mixed-methods research design was employed. Staff found the instrument easy to use and the program-level metrics helpful for planning. Most patients urgently needed a geriatrician assessment (72%) and had moderate to severe cognitive (34%) and functional impairments (34%), depressive symptoms (53%), loneliness (57%), daily pain (32%), and distressed caregivers (46%). Implementation considerations include providing ongoing support and facilitating intersectoral collaboration. The Check Up enhanced the geriatric assessment process by creating a system to track all needs for immediate and future care at both the patient and program level.
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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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".