Standardized Self-Report Tools in Geriatric Medicine Practice: A Quality Improvement Study
Bibliographic record
Abstract
Abstract Comprehensive geriatric assessment (CGA)—a multidimensional diagnostic process to determine medical, cognitive, and functional capacity—has historically included a narrative history supplemented by use of tools to assess domains such as mood or cognition based on assessor preference. This approach to CGA likely works to assess individuals but with increasing clinical complexity and frailty among older adults, a non-standardized approach may mean that key issues are not assessed, and program quality cannot be determined. The COVID-19 pandemic added to these challenges as social distancing practices meant limited face-to-face appointments and use of phone and video assessments. This quality improvement study implemented the interRAI Check-Up Self-Report instrument through a software platform in a specialized geriatric services practice. The instrument can be used over the phone and summarizes specific health problems and needs as well as information about caregiver status and financial trade-offs. Focus groups were also conducted with specialized geriatric services interprofessional team to explore their experiences with implementation. The descriptive analysis of the self-report data revealed expected geriatric issues, such as cognitive and functional impairment, falls and pain. Clients were also commonly experiencing medical instability, cardiorespiratory symptoms, communication impairments, and elevated risk for emergency department visit. Staff found the self-report tool feasible, easy to use, efficient, and the program-level metrics helpful for program planning. In conclusion, introduction of a standardized self-report enhanced CGA by creating a systematic method to flag, track, and prioritize all areas of need for immediate and future care planning at both the client 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 | 0.126 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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 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".