Virtual frailty screening: A quality improvement project to enhance community-based assessment
Bibliographic record
Abstract
Conventional approaches to frailty assessment rely on in-person evaluation by health professionals in medical settings. The COVID-19 pandemic drastically limited access to both primary care and face-to-face appointments, highlighting a need for novel and forward-thinking frailty screening methods to bridge this gap. Community-based seniors-serving organizations are well-positioned to implement remote frailty assessment, given its potential to enhance virtual services and referrals. The Clinical Frailty Scale (CFS) was adapted and implemented as Virtual Frailty Screening (VFS), using the Plan-Do-Study-Act (PDSA) framework, in an inner-city organization for older adults. Participants participated in phone assessments by social workers (SWs); in-person visits included the CFS for comparative purposes. As a component of our larger quality improvement initiative, pilot data were analyzed descriptively alongside inferential methods consisting of Kendall's τb, Χ2-test of association, and Wilcoxon matched-pairs signed-rank test. Over 101 older adults were screened, with 79.21 % being assigned as very mild to moderately frail. VFS usage enabled crucial referrals for 70 older adults to receive long-term frailty-specific support and services in the community. Analysis found that the VFS performed similarly to the CFS, established by high convergent validity and alignment between both scales. Findings support the implementation of remote frailty assessment by SWs in community-based seniors serving organizations. This integration bridges immediate service gaps, introducing a transformative approach to frailty assessment which has the potential for scalability. Success was attributed to a multidisciplinary approach with rapid PDSA changes in a nimble organization.
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 | 0.051 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".