Not Just How Many but Who Is on Shift: The Impact of Workplace Incivility and Bullying on Care Delivery in Nursing Homes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Much of the literature examining the staffing-care quality link in long-term care (LTC) homes focuses on staffing ratios; that is, how many staff are on shift. Far less attention is devoted to exploring the impact of staff members' workplace relationships, or who is on shift. As part of our work exploring workplace incivility and bullying among residential care aides (RCAs), we examined how RCAs' workplace relationships are shaped by peer incivility and bullying and the impact on care delivery. RESEARCH DESIGN AND METHODS: Using critical ethnography, we conducted 100 hr of participant observation and 33 semistructured interviews with RCAs, licensed practical nurses, support staff, and management in 2 nonprofit LTC homes in British Columbia, Canada. RESULTS: Three key themes illustrate the power relations underpinning RCAs' encounters with incivility and bullying that, in turn, shaped care delivery. Requesting Help highlights how exposure to incivility and bullying made RCAs reluctant to seek help from their coworkers. Receiving Help focuses on how power relations and notions of worthiness and reciprocity impacted RCAs' receipt of help from coworkers. Resisting Help/ing outlines how workplace relationships imbued with power relations led some RCAs to refuse assistance from their coworkers, led longer-tenured RCAs to resist helping newer RCAs, and dictated the extent to which RCAs provided care to residents for whom another RCA was responsible. DISCUSSION AND IMPLICATIONS: Findings highlight "who" is on shift warrants as much attention as "how many" are on shift, offering additional insight into the staffing-care quality link.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".