RESUMO
BACKGROUND: Areas for institutional improvement to enhance patient safety are commonly identified by surveying health care workers' (HCWs) attitudes, values, beliefs, perceptions and assumptions regarding institutional practices. An ideal response rate of 100% is rarely achieved in such surveys, and non-response bias can occur when non-respondents differ from respondents on a dimension likely to influence survey conclusions. The conditions for non-response bias to occur can be detected by comparing demographic characteristics of respondents and non-respondents and relating any differences to findings in the literature of differences in the construct of interest as a function of these demographic characteristics. The current study takes this approach. FINDINGS: All 5,609 HCWs at a university medical center were invited to participate in a survey measuring safety and organizational culture (response rate = 53.40%). Respondents indicated their professional group, gender, age group, years of working in the hospital and executive function. Because all HCWs were invited, the demographic composition of the group who did not respond was known. Differences in the demographic composition of respondents and non-respondents were compared using separate Pearson's chi-square tests for each demographic characteristic.Nurses and clinical workers were generally more likely to respond than were physicians, laboratory workers and non-medical workers. Male HCWs were less likely to respond than were females, HCWs aged younger than 45 years old had a lower response rate than did HCWs aged 45 to 54 years old, HCWs who had worked in the hospital for less than 5 years were less likely to respond than were those who had worked in the hospital for 5 years or more and HCWs without an executive function were less likely to respond than were executives. CONCLUSIONS: Demographic characteristics can be linked to response rates and need to be considered in conducting surveys among HCWs. The possibility of non-response bias can be reduced by conducting analyses separately as a function of relevant demographic characteristics, sampling a higher percentage of groups that are known to be less likely to respond, or weighting responses with the reciprocal of the response rate for the respective demographic group.
