In this paper the concepts of general, target and accessible population are explained in response to misconceptions and controversies associated with them, and the fact that the relationships between them have not been explained in the context of qualitative enquiry in any formal study. These concepts are discussed in this study based on a general scenario. We basically attempt to explain the importance of specifying the general, target and accessible populations in a qualitative study when the study population is large. The study depicts how the research goal, contexts and assumptions can dictate the content and concentration of the target and accessible population in qualitative inquiry. It also poses the sampling implications of our explanations and highlights the stages and levels of what we refer to as population refinement.

There is a stark contrast between the activity and disengagement theories in terms of what they say is the level of social engagement and physical activity associated with the population of elderly people. Owing to their opposing arguments, it is confusing to realize that research validates both of them. What their originators and researchers have failed to explain is the unique set of factors or conditions under which each theory is more likely to be confirmed. As a consequence, the contrast between the two theories has a misty practical foundation and therefore remains confounding several years after these theories were formulated. In this paper, an attempt is made to identify conditions under which each theory is more likely to be supported in research. This effort is expected to encourage researchers to relate their findings to the appropriate factors in order to better explain the contrast between the two theories and the practical significance of research results.

This study assessed the behavioral outcomes of Coronavirus 2019 (COVID-19) social distancing protocols and their influences on mental health. An online survey hosted by Survey Monkey was utilized to collect data from residents of three Ghanaian cities of Accra, Kumasi and Tamale. A total of 621 surveys were analyzed, with a sensitivity analysis utilized to select covariates for the regression model. The average age of participants was about 36 years. Findings indicate that reduced physical activity time and a change in sexual activity and smoking frequency are some short-term changes in behavior resulting from social isolation during the lockdown. An increase in sedentary behavior had a negative influence on mental health. For the most part, changes in behaviors in the short-term were associated with lower mental health scores. The study implied that COVID-19 social distancing measures should be implemented alongside public education for discouraging unhealthy changes in behaviors.

From the year 2003 when the first walkability scale was published to date, person-environment fit models and empirical research, some of which was published in Health Promotion International, have encapsulated healthy communities in ‘neighborhood walkability’. While there is no doubt that neighborhood walkability positively influences health-seeking behaviors and health, recent models suggest that their measurement and conceptualization have not emphasized the role played by psychosocial and personal factors in aging in place. Thus, the development of scales measuring human ecosystem factors has not recognized all critical factors suited for older adults. In this paper, we aim to draw on relevant literature to frame a more holistic construct, hereby referred to as Socially Active Neighborhoods (SAN), that would better support aging in place in older populations. Through a narrative review based on a systematic search of the literature, we define the scope of SAN and delineate some contextual implications for gerontology, health promotion and psychometric testing. SAN, unlike neighborhood walkability in its current measurement and conceptualization, incorporates critical theory-informed psychosocial factors (i.e. safety and disability friendliness of neighborhood infrastructure) that can encourage older adults with physiological and cognitive limitations to maintain physical and social activities as well as health in later life. The SAN is the result of our adaptation of key person-environment models, including the Context Dynamics in Aging (CODA) framework, that recognizes the role of context in healthy aging.

Purpose: This study attempted to conceptualize confounding and guide healthcare management researchers to identify and adjust for potential confounders from the point of view of their theoretical frameworks as a basis of achieving internal validity. Design/methodology/approach: A comprehensive statistical procedure was applied to demonstrate how likely it is for misleading effect sizes to be reached in a cross-sectional healthcare management study as a result of failing to adjust for potential confounders. Participants of the study were 403 volunteer health workers in selected hospitals in Accra, Ghana. A self-reported questionnaire was used to gather data. Confirmatory factor analysis and linear regression analyses were used to present findings. Findings: The study revealed that failing to adjust for potential confounders can over- or under-estimate the ‘ultimate effect’, which was under-estimated in this study by a value of β = .006. The auxiliary regression estimates (e.g. the variance) were also wrongly estimated owing to failure to adjust for potential confounders. Originality/value: This study is the first of its kind to address the issue of confounding in healthcare management research and justifies how necessary it is for every study to control for all confounding variables.

Summary: This study examined the moderating influences of active social networks (ASN), sedentary social networks (SSN) and ASN lost on the relationship between neighbourhood walkability and social activity in community-dwelling older adults aged 60 years or more in Accra, Ghana. A total of 863 individuals participated after G*Power 3.1 was utilized to calculate the minimum sample size. We analysed the data with Pearson’s correlation test and hierarchical linear regression models. A sensitivity analysis was conducted to select the ultimate confounding variables. The study found a positive influence of neighbourhood walkability on social activity after the covariate adjustment (β = 0.18; t = 5.2; p = 0.000). The positive influence of neighbourhood walkability on social activity was significantly reduced by ASN lost and SSN. ASN did not have a significant moderating influence on the primary relationship. The study concludes that the positive influence of walkable neighbourhoods on social activity decreases as SSN and ASN lost increase.