BackgroundTwitter’s 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content.ObjectiveThe aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content.MethodsWe conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18.ResultsResults suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA.ConclusionsWe propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts.

Conditions that cause cognitive impairment and behavioural and personality changes, such as Alzheimer's disease (AD) and related dementia, have global impact across cultures. However, the experience of dementia care can vary between individuals, families, formal caregivers, and social groups from various cultures. Self-reported measures, caregiving stress models, and conceptual theories have been developed to address the physical, financial, psychological, and social factors associated with the experience of dementia care. Given the cross-cultural variability in the experience of dementia care, it is important for such methodologies to take individual and cultural construct systems into account. We contend that personal and group constructs associated with dementia care should be explored in both the formal and informal caregiving contexts. Therefore, in this paper we introduce the theory of Personal Construct Psychology (PCP) with its explicit philosophy, well-elaborated theory, and derived assessment methods as a potential constructivist research approach to examine the personal, familial, group, and cultural construct systems that determine the experience of dementia caregiving. These concepts and assessment procedures are illustrated in this paper through case study examples and scenarios from the context of dementia care with a focus on family home caregivers. This paper elaborates the assessment and therapeutic approaches of personal construct theory (PCT) to further expand alternatives for support services and program interventions and to amplify policies for dementia care within and across cultures.

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Background: Most communities' mental health and perceptions of psychological well-being are known to be profoundly disrupted by large-scale pandemics. Despite the wide range of available screening measures, there are few reliable and valid screening measures for detecting psychological symptoms in non-clinical populations during a health emergency situation such as the COVID-19 outbreak. Objective: This study aims to conduct a psychometric analysis of Goldberg's 12-item General Health Questionnaire (GHQ-12) to validate its use among a sample of Saudi adults during the context of COVID-19 lockdown using reliability and factor analyses. Methods: 473 individuals (aged 18 years and over) were recruited and taken from the general Saudi population living in Makkah province of Saudi Arabia to complete the virtual format of the Arabic GHQ-12 (Ar-GHQ-12). In addition to descriptive statistics and reliability analysis, exploratory and confirmatory factor analyses were performed to examine the factorial structure of the Arabic GHQ-12. Results: In line with previous works from several cultures, the Ar-GHQ-12 was found to have high reliability (α = .859) and considered the two-factor solution to be the best-fitting model because it fits the data better than the one-factor (unidimensional) model. Discussion: It was determined that the Ar-GHQ-12 is suitable for assessing the psychological well-being of the general non-psychiatric population in Saudi Arabia in emergency contexts and may be applied in Saudis and other Arabic-speaking populations in research and busy primary care settings.