Association between assisted living facility context and resident pain during the COVID-19 pandemic: A repeated cross-sectional study
Bibliographic record
Abstract
Abstract Background Resident pain has been a common quality issue in congregate care for older adults, and COVID-19-related public health restrictions may have negatively affected resident pain. Most studies have focused on nursing homes (NHs), largely neglecting assisted living (AL). AL residents are at similar risk for pain as NH resident, but with AL providing fewer services and staffing resources. Our study examined whether potentially modifiable AL home characteristics were associated with resident pain during the first two waves of the COVID-19 pandemic. Methods This repeated cross-sectional study linked AL home surveys, collected in COVID-19 waves 1 (March-June 2020) and 2 (October 2020-February 2021) from a key contact, to administrative Resident Assessment Instrument – Home Care (RAI-HC) records in these homes. Surveys assessed preparedness for COVID-19 outbreaks, availability of a registered nurse or nurse practitioner, direct care staff shortages, decreased staff morale, COVID-19 outbreaks, confinement of residents to their rooms, supporting video calls with physicians, facilitating caregiver involvement. The dependent variable (moderate daily pain or pain of a severe intensity) and resident covariates came from the RAI-HC. Using general estimating equations, adjusted for repeated resident assessments and covariates, we assessd whether AL home factors were associated with resident pain during the pandemic. Results We included 985 residents in 41 facilities (wave 1), and 1,134 residents in 42 facilities (wave 2). Pain prevalence [95% confidence interval] decreased non-significantly from 20.6% [18.6%-23.2%] (March-June 2019) to 19.1% [16.9%-21.6%] (October 2020-February 2021). Better preparedness (odds ratio = 1.383 [1.025–1.866]), confinement of residents to their rooms (OR = 1.616 [1.212–2.155]), availability of a nurse practitioner (OR = 0.761 [0.591–0.981]), and staff shortages (OR = 0.684 [0.527–0.888]) were associated with resident pain. Conclusions AL facility-level factors were associated with resident pain during the COVID-19 pandemic. Policy and management interventions can and must address such factors, providing potentially powerful levers for improving AL resident quality of care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".