NURSING HOME STAFFING DISPARITIES IN DISTRESSED COMMUNITIES DURING PANDEMIC
Bibliographic record
Abstract
Abstract This repeated measures study aims to examine the extent of US nursing home staffing disparities in distressed communities during the COVID-19 pandemic. The study used 2020-2022 Nursing Home Compare, 2021 LTCFocus, and the Distressed Community Index from the Economic Innovation Group. Mixed-effects models were used for data analysis. Outcomes were quarterly case-mix adjusted hours per resident day (HPRD) of all nursing staff in the last three quarters of 2020 and all of 2021: registered nurses (RN), licensed practical and vocational nurses (LP/VN), and certified nursing aides (CNA). Predictors included 2020 first quarter nurse staffing levels and distressed community index quintile. Covariates were nursing home size (greater or less than 100 beds), 5-star overall rating, Medicaid payer percentage, and non-profit status. Of the 13,845 nursing homes, 70% were for-profit and 50% had over 100 beds. On average, 5-star overall rating of nursing homes was 3.19 (sd=1.41) and Medicaid payer percentage was 60% (sd=23.5%). Compared to pre-pandemic nurse HPRD, average HPRD of the subsequent 7 quarters declined (differences: Total=-0.67, 17%; RN=-0.30, 43%; LP/VN=-0.13, 15%; CNA=-0.26, 11%). Pandemic HPRD were negatively associated with pre-pandemic HPRD (slopes: Total= -0.078; RN= -0.039; LP/VN= -0.010; CNA= -0.062, all p< 0.001). Modeled interactions suggest that declines in HPRD were greatest in the most distressed communities. Analyses showed declines in nursing home staffing levels during the pandemic, with the lowest HPRD and greatest declines observed in the most distressed communities and RNs. Practical significance of the predictors and resident outcomes requires further examination.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".