ASSOCIATION OF ASSISTED LIVING HOMES PREPAREDNESS FOR AND RESPONSES TO THE COVID-19 PANDEMIC WITH RESIDENT PAIN
Bibliographic record
Abstract
Abstract The COVID-19 pandemic severely disrupted care processes in assisted living (AL) and negatively affected resident outcomes associated with resident pain (e.g., mobility problems, depression). Pain has severe consequences, including hopelessness, insomnia, depression, poor quality of life. However, we lack research on how the pandemic affected AL resident pain. To address this gap, we linked surveys from 42 AL homes in Alberta, reflecting pandemic waves 1 (Mar-Jul 2020) and 2 (Nov 2020-Feb 2021), to the Resident Assessment Instrument (RAI) records of 1,828 residents (wave 1: 890, wave 2: 938) who lived in these homes during these periods. Using generalized estimating equation models, we assessed whether resident characteristics, physical and occupational therapy received, home preparedness for and responses to the pandemic were associated with resident pain (measured as at least moderate daily pain or pain of excruciating intensity, based on the RAI pain items). Over 19% of the residents reported pain (wave 1: 19%, wave 2: 19.1%). Resident characteristics associated with pain were cognitive impairment (OR=0.4, 95% CI: 0.3-0.6), loneliness (OR=1.8, 95% CI: 1.3-2.6), arthritis (OR=2.1, 95% CI: 1.6-2.8), fractures (OR=1.97, 95% CI: 1.4-2.9), polypharmacy (OR=1.82, 95% CI: 1.3-2.6) and use of analgesics (OR=1.76, 95% CI: 1.3-2.3). Home preparedness and physical and occupational therapy received were not associated with pain, but more communication with family/friend caregivers (OR=0.41, 95% CI: 0.2-0.8) was. Effective communication of family/friends with residents may promote better management of residents’ pain. Further, longitudinal studies examining resident and AL home characteristics and its impact on residents’ pain are needed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".