The Complexity of Burnout Experiences among Care Aides: A Person-Oriented Approach to Burnout Patterns
Bibliographic record
Abstract
Care aides working in nursing homes experience burnout attributed to various workplace stressors. Burnout dimensions (exhaustion, cynicism, and reduced professional efficacy) interact to form distinct burnout patterns. Using a person-oriented approach, we aimed to identify burnout patterns among care aides and to examine their association with individual and job-related factors. This was a cross-sectional, secondary analysis of the Translating Research in Elder Care 2019–2020 survey data collected from 3765 care aides working in Canadian nursing homes. We used Maslach Burnout Inventory to assess burnout and performed latent profile analysis to identify burnout patterns, then examined their associations with other factors. We identified an engaged pattern (43.2% of the care aide sample) with low exhaustion and cynicism and high professional efficacy; an overwhelmed but accomplished pattern (38.5%) with high levels of the three dimensions; two intermediate patterns—a tired and ineffective pattern (2.4%) and a tired but effective pattern (15.8%). The engaged group reported the most favorable scores on work environment, work-life experiences, and health, whereas the tired and ineffective group reported the least favorable scores. The findings suggest complex experiences of burnout among care aides and call for tailored interventions to distinct burnout patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".