ABSTRACT
Although there has been speculation about gender differences in retirement decisions, research directly on the issue has been sparse, and results have been inconsistent. This study, which examined hypotheses based on traditional gender roles, is one of the few to examine the interactions between retirement predictors and gender. In a random sample of older employees and retirees from a large midwestern manufacturing organization, retirement decisions differed between men and women primarily when dependents lived in the household, when the health of one's spouse was a consideration, and when one's spouse was retired. These differences appeared to be partially dependent on the operational definition of retirement.
Subject(s)
Retirement/psychology , Aged , Employment , Family Characteristics , Female , Health Status , Humans , Logistic Models , Male , Middle Aged , Odds Ratio , Sex FactorsABSTRACT
The goal of physician referral services is to schedule appointments, but if too many patients fail to show up, the value of the service will be compromised. The authors found that appointment breaking can be predicted by the number of days to the scheduled appointment, the doctor's specialty, and the patient's age and gender. They also offer specific suggestions for modifying the marketing mix to reduce the incidence of no-shows.
Subject(s)
Appointments and Schedules , Patient Acceptance of Health Care/statistics & numerical data , Referral and Consultation/statistics & numerical data , Adolescent , Adult , Age Factors , Chi-Square Distribution , Child , Data Collection , Family Practice/statistics & numerical data , Female , Forecasting , Humans , Male , Medicine/statistics & numerical data , New England , Program Evaluation/statistics & numerical data , Sex Factors , Specialization , Time Factors , Urban HealthABSTRACT
From a review of research on health care appointment breaking, the authors find that patient demographic characteristics, psychosocial problems, previous appointment keeping, health beliefs, and situational factors predict no-show behavior. Suggestions are offered for designing the marketing mix to increase patient appointment keeping. Methods for mitigating the negative effects of no-shows on health care providers are described.
