Job Satisfaction among Care Aides in Residential Long-Term Care: A Systematic Review of Contributing Factors, Both Individual and Organizational
Bibliographic record
Abstract
Despite an increasing literature on professional nurses' job satisfaction, job satisfaction by nonprofessional nursing care providers and, in particular, in residential long-term care facilities, is sparsely described. The purpose of this study was to systematically review the evidence on which factors (individual and organizational) are associated with job satisfaction among care aides, nurse aides, and nursing assistants, who provide the majority of direct resident care, in residential long-term care facilities. Nine online databases were searched. Two authors independently screened, and extracted data and assessed the included publications for methodological quality. Decision rules were developed a priori to draw conclusions on which factors are important to care aide job satisfaction. Forty-two publications were included. Individual factors found to be important were empowerment and autonomy. Six additional individual factors were found to be not important: age, ethnicity, gender, education level, attending specialized training, and years of experience. Organizational factors found to be important were facility resources and workload. Two additional factors were found to be not important: satisfaction with salary/benefits and job performance. Factors important to care aide job satisfaction differ from those reported among hospital nurses, supporting the need for different strategies to improve care aide job satisfaction in residential long-term 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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".