Using standard clinical assessments for home care to identify vulnerable populations before, during, and after disasters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Several studies have shown the increased vulnerability of and disproportionate mortality rate among frail community-dwelling older adults as a result of emergencies and disasters. This article will discuss the applicability of the Vulnerable Persons at Risk (VPR) and VPR Plus decision support algorithms designed based on the Resident Assessment Instrument-Home Care (RAI-HC) to identify the most vulnerable community-dwelling (older) adults. DESIGN: A sample was taken from the Ontario RAI-HC database by selecting unique home care clients with assessments closest to December 31, 2014 (N = 275,797). Statistical methods used include cross tabulation, bivariate logistic regression as well as Kaplan-Meier survival plotting and Cox proportional hazards ratios calculations. RESULTS: The VPR and VPR Plus algorithms, were highly predictive of mortality, long-term care admission and hospitalization in ordinary circumstances. This provides a good indication of the strength of the algorithms in identifying vulnerable persons at times of emergencies. CONCLUSIONS: Access to real-time person-level information of persons with functional care needs is a vital enabler for emergency responders in prioritizing and allocating resources during a disaster, and has great utility for emergency planning and recovery efforts. The development of valid and reliable algorithms supports the rapid identification and response to vulnerable community-dwelling persons for all phases of emergency management.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it