Background The rapid proliferation of web-based information on health and health care has profoundly changed individuals’ health-seeking behaviors, with individuals choosing the internet as their first source of information on their health conditions before seeking professional advice. However, barriers to the evaluation of people’s eHealth literacy present some difficulties for decision makers with respect to encouraging and empowering patients to use web-based resources. Objective This study aims to examine the psychometric properties of a simplified Chinese version of the eHealth Literacy Scale (SC-eHEALS). Methods Data used for analysis were obtained from a cross-sectional multicenter survey. Confirmatory factor analysis (CFA) was used to examine the structure of the SC-eHEALS. Correlations between the SC-eHEALS and ICEpop capability measure for adults (ICECAP-A) items and overall health status were estimated to assess the convergent validity. Internal consistency reliability was confirmed using Cronbach alpha (α), McDonald omega (ω), and split-half reliability (λ). A general partial credit model was used to perform the item response theory (IRT) analysis. Item difficulty, discrimination, and fit were reported. Item-category characteristic curves (ICCs) and item and test information curves were used to graphically assess the validity and reliability based on the IRT analysis. Differential item functioning (DIF) was used to check for possible item bias on gender and age. Results A total of 574 respondents from 5 cities in China completed the SC-eHEALS. CFA confirmed that the one-factor model was acceptable. The internal consistency reliability was good, with α=0.96, ω=0.92, and λ=0.96. The item-total correlation coefficients ranged between 0.86 and 0.91. Items 8 and 4 showed the lowest and highest mean scores, respectively. The correlation coefficients between the SC-eHEALS and ICECAP-A items and overall health status were significant, but the strength was mild. The discrimination of SC-eHEALS items ranged between 2.63 and 5.42. ICCs indicated that the order of categories’ thresholds for all items was as expected. In total, 70% of the information provided by SC-eHEALS was below the average level of the latent trait. DIF was found for item 6 on age. Conclusions The SC-eHEALS has been demonstrated to have good psychometric properties and can therefore be used to evaluate people’s eHealth literacy in China.
Background Although previous studies have shown that a high level of health literacy can improve patients’ ability to engage in health-related shared decision-making (SDM) and improve their quality of life, few studies have investigated the role of eHealth literacy in improving patient satisfaction with SDM (SSDM) and well-being. Objective This study aims to assess the relationship between patients’ eHealth literacy and their socioeconomic determinants and to investigate the association between patients’ eHealth literacy and their SSDM and well-being. Methods The data used in this study were obtained from a multicenter cross-sectional survey in China. The eHealth Literacy Scale (eHEALS) and Investigating Choice Experiments Capability Measure for Adults were used to measure patients’ eHealth literacy and capability well-being, respectively. The SSDM was assessed by using a self-administered questionnaire. The Kruskal-Wallis one-way analysis of variance and Wilcoxon signed-rank test were used to compare the differences in the eHEALS, SSDM, and Investigating Choice Experiments Capability Measure for Adults scores of patients with varying background characteristics. Ordinary least square regression models were used to assess the relationship among eHealth literacy, SSDM, and well-being adjusted by patients’ background characteristics. Results A total of 569 patients completed the questionnaire. Patients who were male, were highly educated, were childless, were fully employed, were without chronic conditions, and indicated no depressive disorder reported a higher mean score on the eHEALS. Younger patients (SSDM≥61 years=88.6 vs SSDM16-30 years=84.2) tended to show higher SSDM. Patients who were rural residents and were well paid were more likely to report good capability well-being. Patients who had a higher SSDM and better capability well-being reported a significantly higher level of eHealth literacy than those who had lower SSDM and poorer capability well-being. The regression models showed a positive relationship between eHealth literacy and both SSDM (β=.22; P<.001) and well-being (β=.26; P<.001) after adjusting for patients’ demographic, socioeconomic status, lifestyle, and health status variables. Conclusions This study showed that patients with a high level of eHealth literacy are more likely to experience optimal SDM and improved capability well-being. However, patients’ depressive status may alter the relationship between eHealth literacy and SSDM.
This study aimed to validate the simplified Chinese version of the Toronto Empathy Questionnaire (cTEQ) for use with the Chinese population. The original English version of the TEQ was translated into simplified Chinese based on international criteria. Psychometric analyses were performed based on three psychometric methods: classical test theory (CTT), item response theory (IRT), and Rasch model theory (RMT). Differential item functioning analysis was adopted to check possible item bias caused by responses from different subgroups based on sex and ethnicity. A total of 1296 medical students successfully completed the TEQ through an online survey; 75.2% of respondents were female and the average age was 19 years old. Forty students completed the questionnaire 2 weeks later to assess the test-retest reliability of the questionnaire. Confirmatory factor analysis supported a 3-factor structure of the cTEQ. The CTT analyses confirmed that the cTEQ has sound psychometric properties. However, IRT and RMT analyses suggested some items might need further modifications and revisions.
Background: The objectives of this study were two-fold: (1) to assess the relationship between patients' decisional regret and their well-being and (2) to examine the mediated effect of shared decision-making (SDM) on this relationship.Methods: A cross-sectional survey was conducted in five cities in Southern China. Patients were asked to fill out questionnaires assessing their decisional regret, SDM, subjective well-being, and depressive status. Mediation analysis was used to investigate the effect of SDM on the relationship between patients' decisional regret and their subjective well-being.Results: The findings showed significant direct negative effects of decisional regret on subjective well-being and SDM. For non-depressive patients, SDM exerted a significant and indirect effect on reducing the negative influence of decisional regret on subjective well-being.Conclusions: Findings suggest that implementation of SDM can decrease patients' decisional regret and improve their well-being; however, there is a need to examine their depressive status as part of routine healthcare.
ObjectiveThe objective of this study was to evaluate the psychometric properties of the Chinese version of the decision regret scale (DRSc).MethodsThe data of 704 patients who completed the DRSc were used for the analyses. We evaluated the construct, convergent/discriminant, and known-group validity; internal consistency and test–retest reliability; and the item invariance of the DRSc. A receiver operating characteristic (ROC) curve was employed to confirm the optimal cutoff point of the scale.ResultsA confirmatory factor analysis (CFA) indicated that a one-factor model fits the data. The internal consistency (α = 0.74) and test–retest reliability [intraclass correlation coefficient (ICC) = 0.71] of the DRSc were acceptable. The DRSc demonstrated unidimensionality and invariance for use across the sexes. It was confirmed that an optimal cutoff point of 25 could discriminate between patients with high and low decisional regret during clinical practice.ConclusionThe DRSc is a parsimonious instrument that can be used to measure the uncertainty inherent in medical decisions. It can be employed to provide knowledge, offer support, and elicit patient preferences in an attempt to promote shared decision-making.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.