Transforming Retirement: New Definitions of Life After Work

This quantitative research study uses survey data of women born between 1946 and 1951 in Australia. It follows earlier work that identified the importance of transitions from work for women of the baby boomer generation. We provide important insights into the lives of women who have partially or fully retired and the changing nature of women's work and retirement. For many women, retirement is characterized by newfound freedoms, opportunities, career change, and evolving identities, yet others view retirement as a continuation of previous occupational and gendered roles and commitments. This study has important implications for retirement policies for women.     

Changing the risky beliefs of post-partum women about therapeutic sun-exposure

BackgroundMany post-partum women hold risky beliefs about perceived therapeutic benefits of sun-exposure in the post-partum period and infancy.QuestionCan a maternity hospital based educational intervention reduce the prevalence of such beliefs among post-partum women?MethodsIn this outcome evaluation of an interventional study, two groups of healthy post-partum women (hospital inpatients) were interviewed, 1–4 days following delivery. The first cross-section (106 women) was recruited prior to in-services for maternity staff; the second (203 women) was recruited after completion of the in-services. Data were compared between the groups.FindingsMore pre-intervention than post-intervention women reported they would expose their baby to sunlight to treat suspected jaundice (28.8% vs 13.3%; p < 0.001) or help his/her skin adapt to the sun (10.5% vs 2.5%; p = 0.003); or use sunlight to manage breastfeeding-associated sore/cracked nipples (7.6% vs 2%; p = 0.026).ConclusionThis simple, effective educational intervention could be implemented in programmes for parents, health professionals and students.     

From “nobody's clapping for us” to “bad moms”: COVID-19 and the circle of childcare in Canada

The COVID-19 pandemic has highlighted the importance of childcare to national economies in general and women's economic participation in particular, spurring renewed interest in childcare policy in many countries that have implemented lockdowns. This paper adopts a circle of care framework to analyzes how COVID-19 has affected paid childcare, unpaid childcare and other paid work, and the relationship between these sectors. Analysis is grounded in the lived experiences of parents and childcare educators, documented through 16 semi-structured interviews during the initial lockdown (March–June 2020) in British Columbia, Canada. Experiences from educators suggest their safety was not prioritized, and that their contributions were undervalued and went unrecognized. Mothers, who provided the majority of unpaid care, not only lost income due to care demands, but struggled to access necessities, with some reporting increased personal insecurity. Those attempting to work from home also experienced feelings of guilt and distress as they tried to manage the triple burden. Similarities of experiences across the circle of care suggest the COVID-19 childcare policy response in BC Canada downloaded care responsibilities on to women without corresponding recognition or support, causing women to absorb the costs of care work, with potential long-term negative effects on women's careers and well-being, as well as on the resilience of the circle of care. Pandemic recovery and preparedness policies that aim to promote gender equality must consider all sectors of the circle of care and the relationships between them.     

Nonverbal Auditory Cues Allow Relationship Quality to be Inferred During Conversations

Journal of Nonverbal Behavior - The claim that nonverbal cues provide more information than the linguistic content of a conversational exchange (the Mehrabian Conjecture) has been widely cited and...     

Splines as a Useful and Convenient Statistical Tool

The framework for a unified statistical theory of spline regression assuming fixed knots using the truncated polynomial or “+” function representation is presented. In particular, a partial ordering of some spline models is introduced to clarify their relationship and to indicate the hypotheses that can be tested by using either standard multiple regression procedures or a little-used conditional test developed by Hotelling (1940). The construction of spline models with polynomial pieces of different degrees is illustrated. A numerical example from a chemical experiment is given by using the GLM procedure of the statistical software package SAS (Barr et al. 1976).     

A Simplified Treatment of the Estimation of Parameters and Tests of Hypotheses in Constrained Design Models with Unbalanced Data

A simple approach to the least squares estimation of parameters in the linear design model is given. An algebraically tractable example for unbalanced data is used to illustrate the approach.     

Do Baseball Players Regress toward the Mean?

Baseball performances are an imperfect measure of baseball abilities, and consequently exaggerate differences in abilities. Predictions of relative batting averages and earned run averages can be improved substantially by using correlation coefficients estimated from earlier seasons to shrink performances toward the mean.     

Model selection for logistic regression via association rules analysis

Interaction is very common in reality, but has received little attention in logistic regression literature. This is especially true for higher-order interactions. In conventional logistic regression, interactions are typically ignored. We propose a model selection procedure by implementing an association rules analysis. We do this by (1) exploring the combinations of input variables which have significant impacts to response (via association rules analysis); (2) selecting the potential (low- and high-order) interactions; (3) converting these potential interactions into new dummy variables; and (4) performing variable selections among all the input variables and the newly created dummy variables (interactions) to build up the optimal logistic regression model. Our model selection procedure establishes the optimal combination of main effects and potential interactions. The comparisons are made through thorough simulations. It is shown that the proposed method outperforms the existing methods in all cases. A real-life example is discussed in detail to demonstrate the proposed method.