Measurement instruments are used to collect data about respondents. In social pharmacy, measurement instruments are often used to measure latent constructs, such as attitudes, among healthcare professionals and patients. This paper aims to describe the fundamental aspects of designing and validating instruments, which aim to measure latent constructs. The main focus of this manuscript is to describe the considerations and processes relating to exploratory and confirmatory factor analyses, when used to develop measures of latent psychosocial constructs. However, it also presents a detailed summary of the current evidence and suggestions for item generation and sample selection, as well as, an in-depth description of approaches to content and face validation. Suggestions for further reading are also provided.

No abstract

Background People engage in health information–seeking behavior to support health outcomes, and being able to predict such behavior can inform the development of interventions to guide effective health information seeking. Obtaining a comprehensive list of the predictors of health information–seeking behavior through a systematic search of the literature and exploring the interrelationship of these predictors are critical first steps in this process. Objective This study aims to identify significant predictors of health information–seeking behavior in the primary literature, develop a common taxonomy for these predictors, and identify the evolution of the concerned research field. Methods A systematic search of PsycINFO, Scopus, and PubMed was conducted for all years up to and including December 10, 2019. Quantitative studies identifying significant predictors of health information–seeking behavior were included. Information seeking was broadly defined and not restricted to any source of health information. Data extraction of significant predictors was performed by 2 authors, and network analysis was conducted to observe the relationships between predictors with time. Results A total of 9549 articles were retrieved, and after the screening, 344 studies were retained for analysis. A total of 1595 significant predictors were identified. These predictors were categorized into 67 predictor categories, with the most central predictors being age, education, gender, health condition, and financial income. With time, the interrelationship of predictors in the network became denser, with the growth of new predictor grouping reaching saturation (1 new predictor identified) in the past 7 years, despite increasing publication rates. Conclusions A common taxonomy was developed to classify 67 significant predictors of health information–seeking behavior. A time-aggregated network method was developed to track the evolution of the research field, showing the maturation of new predictor terms and an increase in primary studies reporting multiple significant predictors of health information–seeking behavior. The literature has evolved with a decreased characterization of novel predictors of health information–seeking behavior. In contrast, we identified a parallel increase in the complexity of predicting health information–seeking behavior, with an increase in the literature describing multiple significant predictors.

No abstract

No abstract