Burnout Among Nursing Home Care Aides and the Effects on Resident Outcomes
Bibliographic record
Abstract
While burnout among health care workers has been well studied, little is known about the extent to which burnout among health care workers impacts the outcomes of their care recipients. To test this, we used a multi-year (2014-2020) survey of care aides working in approximately 90 nursing homes (NHs); the survey focused on work-life measures, including the Maslach Burnout Inventory (MBI) and work-unit identifier. Resident Assessment Instrument Minimum Data Set (RAI-MDS 2.0) data were obtained on all residents in the sampled NHs during this time and included a unit identifier for each resident. We used multi-level models to test associations between the MBI emotional exhaustion and cynicism sub-scales reported by care aides and the resident outcomes of antipsychotics without indication, depressive symptoms, and responsive behaviors among residents on units. In 2019/2020, our sample included 3,547 care aides and 10,117 residents in 282 units. The mean frequency of emotional exhaustion and cynicism across units was 43% and 50%, respectively. While residents frequently experienced antipsychotics without indication 1,852 (18.3%), depressive symptoms 2,089 (20.7%), and responsive behaviors 3,891 (38.5%), none were found to be associated with either emotional exhaustion or cynicism among care aides.
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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 | 0.005 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".