Organizational Climate of Staff Working Conditions and Safety-An Integrative Model

Abstract

Objectives: This project sought to compare measures of organizational climate in ongoing patient safety studies, identify similarities and setting-specific dimensions, develop a model of climate domains that are hypothesized to affect outcomes across settings, and test aspects of the model. Methods: Investigators who had surveyed health care workers' perceptions of organizational climate in six studies funded by the Agency for Healthcare Research and Quality (AHRQ) were invited to participate. Survey items from each study were classified using four climate domains found in a prior literature review. The authors discussed subconstructs, proposed additional constructs, developed an integrative model, and independently tested selected aspects of the model. Results: The investigators who participated had studied acute care, home health care, long-term care, and multiple settings; two investigators had studied primary care. More than 80,000 workers were surveyed. The model's core climate domains included leadership (e.g., values) and organizational structural characteristics (e.g., communication processes and information technology), the impact of which was mediated by four process variables: supervision, group behavior (e.g., collaboration), quality emphasis (e.g., patient centeredness), and work design (e.g., staffing). These factors affect health care worker outcomes (e.g., satisfaction and intention to leave) and patient outcomes. Overall, the full model explained 24 to 65 percent of the variance in employee satisfaction, but was not as effective at predicting intention to leave. Conclusions: While some of these domains appeared in prior models, new domains—quality emphasis, new subconstructs, information technology, and patient centeredness—are emerging. Our model invites dialogue among researchers and informs agenda-setting for future research into organizational climate and the safety of patients and health care employees. This integrative model will facilitate cross-study quantification of associations among variables in these important domains.