Recovering After COVID-19: A Comparison of Burnout Levels Among Care Aides From 2014 to 2024
Bibliographic record
Abstract
OBJECTIVES: To examine recovery (prepandemic to postpandemic), specifically related to burnout for care aides working in nursing homes. DESIGN: This repeated cross-sectional study used 5 data collection points spanning 10 years (2014-2024) collected by Translating Research in Elder Care (TREC). Time points were prepandemic: T1 (September 2014 to May 2015), T2 (May to December 2017), and T3 (September 2019 to March 2020); pandemic: T4 (June 2021to September 2021); and postpandemic: T5 (September 2023 to May 2024). SETTING AND PARTICIPANTS: Participants were health care aides (care aides) working in nursing homes in the urban health zones of Calgary and Edmonton in the province of Alberta, Canada. METHODS: Measurements included demographic variables, unit and nursing home characteristics, and burnout, specifically the Maslach Burnout Inventory, short form 9 (MBI-GS9). The MBI has 3 subscales, emotional exhaustion, cynicism, and professional efficacy. We used descriptive statistics to describe the sample characteristics. We used hierarchical linear models (3 levels) to account for the nested structure of data to examine the change in burnout over time and examine factors associated with it. RESULTS: Our total sample for each time point was as follows: T1 (n = 1620), T2 (n = 1789), T3 (n = 1590), T4 (n = 760), and T5 (n = 1727). Comparing burnout levels prepandemic to postpandemic showed that care aides' level of emotional exhaustion postpandemic was higher than prepandemic and that their level of professional efficacy was lower, which was statistically significant. Care aides' age and shift often worked were significantly associated with emotional exhaustion, cynicism, and professional efficacy. CONCLUSIONS AND IMPLICATIONS: Care aides have not fully recovered to prepandemic burnout levels, specifically their emotional exhaustion and professional efficacy levels. This study has important implications for the retention of this essential workforce in nursing homes.
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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 | 0.002 |
| 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 | 0.001 |
| 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".