Shift work generally is defined as work hours that are scheduled outside of daylight. Shift work disrupts the synchronous relationship between the body's internal clock and the environment. The disruption often results in problems such as sleep disturbances, increased accidents and injuries, and social isolation. Physiologic effects include changes in rhythms of core temperature, various hormonal levels, immune functioning, and activity-rest cycles. Adaptation to shift work is promoted by reentrainment of the internally regulated functions and adjustment of activity-rest and social patterns. Nurses working various shifts can improve shift-work tolerance when they understand and adopt counter measures to reduce the feelings of jet lag. By learning how to adjust internal rhythms to the same phase as working time, nurses can improve daytime sleep and family functioning and reduce sleepiness and work-related errors. Modifying external factors such as the direction of the rotation pattern, the number of consecutive night shifts worked, and food and beverage intake patterns can help to reduce the negative health effects of shift work. Nurses can adopt counter measures such as power napping, eliminating overtime on 12-hour shifts, and completing challenging tasks before 4 am to reduce patient care errors.

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Characteristics of shiftwork schedules have implications for off-shift well-being. We examined the extent to which several shift characteristics (e.g., shift length, working Sundays) are associated with three aspects of off-shift well-being: work-to-family conflict, physical well-being, and mental wellbeing. We also investigated whether these relationships differed in four nations. The Survey of Work and Time was completed by 906 healthcare professionals located in Australia, Brazil, Croatia, and the USA. Hierarchical multiple regression analyses supported the hypothesis that shiftwork characteristics account for significant unique variance in all three measures of well-being beyond that accounted for by work and family demands, and personal characteristics. The patterns of regression weights indicated that particular shiftwork characteristics have differential relevance to indices of work-to-family conflict, physical well-being, and mental well-being. Our findings suggest that healthcare organizations should carefully consider the implications of shiftwork characteristics for off-shift well-being. Furthermore, although our findings did not indicate national differences in the nature of relationships between shift characteristics and well-being, shiftwork characteristics and demographics for healthcare professionals differ in systematic ways among nations; as such, effective solutions may be context-specific.

The concept of lack of anonymity was updated; portions of the original definition remain unchanged. Empirical referents reveal the defining attributes in daily life and may guide future research on the effect of lack of anonymity on nursing practice. This analysis advances the conceptual understanding of rural nursing theory.

An increasing number of ethnic minorities are expected to enter the United States workforce based on projected demographic changes. This includes American Indian/Alaskan Native (AI/AN) nurses. Sociocultural influences on sleep disturbances, sleepiness, and other aspects related to shift-work tolerance are of unrecognized importance. More minority nurses are needed to provide culturally congruent care; however, AI/AN nurses represent less than 1% of nurses located throughout the American workforce. This article aims to verify the feasibility of Internet data collection (Web-based survey) methods and instrument stability as the first part of a two-phase study comparing individual differences and shift-work-related sleep disturbances between AI/AN and White non-Hispanic (WNH) nurses. In the first phase, an Internet survey was used to reach a cross-section of AI/AN and WNH nurses. The on-line survey was composed of accepted shift-work-related instruments. Items estimating sleep disturbances, sociocultural choices, time awareness, polychronicity, morningness/ eveningness, ethnic identity, and demographic questions were asked. The survey was linked to a series of Web pages describing the study purpose, inclusion and exclusion criteria, consent form, Web survey, and the second phase of the study in which subjects were invited to participate in actigraphy measurements. The survey was pilot-tested for error codes, item confusion, length, and completion time. Forced-answer questions were added asking ethnicity, age group, license type, state where licensed, and legal name on nursing license before accessing the survey. Data were saved periodically, cued by the word "continue." The database was located on a secure server and password protected. Nurses were recruited using published articles and printed advertisements, hospital e-mail systems, national nursing organization Web sites (minoritynurse.com; NANAINA.org), nursing Web site discussion groups, snow-balling, and word of mouth. The site was accessed 656 times with the Internet survey being completed by 138 WNH and 56 AI/AN nurses meeting the inclusion criteria. Except for the polychronicity measure (PAI3), instruments measuring time awareness, chronotype, and situational sleepiness achieved acceptable reliability coefficients with Internet data collection. Using pull-down menus would improve questions asking specific times. Internet data collection with different ethnic groups is possible; however, accessing the target population may be difficult. Despite extensive recruitment efforts, few AI/AN nurses participated. Computer literacy and failing to relate to the study's purpose may have limited the interest of the AI/AN nurses. It is possible to recruit nurse shift workers and collect individual difference and sleep disturbance data through the Internet; however, the researcher must remain vigilant throughout the process.