Staff Turnover Intention at Long-Term Care Facilities: Implications of Resident Aggression, Burnout, and Fatigue
Bibliographic record
Abstract
OBJECTIVES: Staff shortages and the high turnover rate of nursing assistants pose great challenges to long-term care. This study examined the effects of aggression from residents of long-term care facilities, burnout, and fatigue on staff turnover intention. The findings will help managers to devise effective measures to retain their staff. DESIGN: Cross-sectional descriptive study design. SETTING AND PARTICIPANTS: A total of 800 nursing assistants were recruited from 70 long-term care facilities using convenience sampling. METHODS: The participants were individually interviewed and provided information about their turnover intention, resident aggression witnessed and experienced, self-efficacy, neuroticism, burnout, fatigue, and personal and facility characteristics. RESULTS: Hierarchical multiple regression analysis revealed that the size and organizational practices of long-term care facilities were not associated with staff turnover intention. Staff who spent less time in the industry reported witnessing resident-to-resident aggression, experienced resident-to-staff aggression, reported high levels of burnout, had acute or chronic fatigue, and had low levels of inter-shift recovery were more likely than others to report a high turnover intention. CONCLUSIONS AND IMPLICATIONS: Staff turnover poses great challenges to staff, residents, and organizations. This study identified important factors that may help support staff in long-term care facilities. Specific measures, such as person-centered care to diminish resident aggression by addressing residents' unmet needs, work-directed programs to mitigate burnout and improve staff mental health, and flexible schedules to prevent fatigue should also be advocated to prevent staff turnover.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".