HRM Practices, Organizational Culture and Nurse Staffing Turnover among High-Medicaid Nursing Homes
Bibliographic record
Abstract
Nurse staffing turnover has been associated with nursing home quality, such as worse resident outcomes, higher rates of infection, and hospitalizations. Nurse staffing turnover can be worse in under-resourced nursing homes, such as those with a high proportion of Medicaid residents. These organizations may lack the resources to attract and retain nurse staff, and this could contribute to higher nurse turnover. However, even within this group of under-resourced nursing homes, we observe variations in nurse turnover rates. Using the resource-based view of the firm (RBV), we explored the role that both human resource management (HRM) practices and organizational culture may have in explaining the variations in nursing staff turnover among high Medicaid nursing homes. Data consisted of survey and secondary data sources for 2017-2018. Survey data comprised 348 responses (33% response rate) from a national mailer of US nursing home administrators in high-Medicaid (85% or higher) facilities. Survey data were merged with secondary datasets including Long-Term Care Focus (LTCFocus) and the Area Health Resource File (AHRF). The dependent variables (nurse staffing turnover rates) consisted of the percentages of registered nurses (RNs)/licensed practical nurses (LPNs)/certified nurse aides (CNAs) that had voluntarily quit the organization during the past year. The first independent variable, HRM practices, consisted of three sub-scales: traditional, employee-centered, and high involvement practices. The second independent variable consisted of nursing homes’ classification into one of four organizational cultures: clan, market, hierarchical, and non-dominant. Organizational and market variables were controlled for. Data were modeled using Poisson log-linear regression, and propensity score weights were used to adjust for potential survey non-response bias. Results show that high involvement HRM practices and having a clan culture (compared to a market, hierarchical, and non-dominant culture) are associated with lower RN, LPN, and CNA staffing turnover. The findings of this study highlight the importance of organizational culture as well as HRM practices for practitioners and policy makers to more effectively target their efforts to reduce nurse turnover in high Medicaid nursing homes. By reducing turnover, these under-resourced facilities may be able to lower costs and improve their financial performance, which may ultimately reduce the likelihood of closure with implications for access to long-term care. Similarly, lower nurse staffing turnover rates may improve quality of care for a particularly vulnerable nursing home population, i.e. those with lower SES and racial/ethnic minorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".