Development and Psychometric Evaluation of a Bilingual Instrument for Assessing Beliefs Affecting Health-Seeking Intentions in Cognitive Decline Among Latino Populations
Mora Pinzon, M. C.; Sayegh, P.; Fernandez de Cordova, S.; Brown, R.; Barrett, B.
Show abstract
BackgroundThe Latino community in the United States is disproportionately affected by Alzheimers disease and related dementias (ADRD). However, there are no instruments to assess health seeking behaviors in this population. This study describes the development and validation process of a new 35-item instrument, "Beliefs affecting health Seeking Intentions in Cognitive decline (BESIC)", available in both English and Spanish. MethodsThe psychometric analysis involved assessing the goodness of fit of three measurement models; congeneric model, tau-equivalency, and the parallel model. Multigroup Confirmatory Factor Analysis (MGCFA) was used to check if the same factors are being measured in the same way across different groups. ResultsThe partial tau-equivalent model provided the best fit for our data, suggesting that all items in the instrument measure the same underlying concept construct with different degrees of precision and error. The instrument demonstrated good reliability for all sub-domains in the total sample, as well as for the two language surveys. While average scores in the two language groups were somewhat different, MGCFA demonstrated that the instrument works well and similarly in both English and Spanish. ConclusionThese psychometric validation findings suggest that BESIC is a useful tool for measuring beliefs affecting health-seeking intentions during cognitive decline in both English and Spanish-speaking populations in the United States. However, while the overall structure of the instrument was equivalent across languages, the strength of these relationships and the average scores on the items were not. This suggests that the way individuals from different language groups respond to the items may vary, requiring further investigation. This will help refine the instrument further and ensure its accuracy and usefulness in future research and practice.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Feasibility and reliability of online vs in-person cognitive testing in healthy older people 94%
- Impact of the COVID-19 pandemic on cognitive function in Japanese community-dwelling older adults in a class for preventing cognitive decline 94%
- Physical body experiences questionnaire simplified for active aging (PBE-QAG): Validation with Rasch measurement theory 93%
Similar papers in this journal
- Validation of the German version of the Life-Space Assessment LSA-D 94%
- The Longitudinal Aging Study Amsterdam COVID-19 exposure index: a cross-sectional analysis of the impact of the pandemic on daily functioning of older adults 93%
- The impact of patient-centered care on quality of life and hope among patients receiving home medical care: The Zaitaku Evaluative Initiatives and Outcome Study 93%
Similar papers in this journal
- Acceptability and feasibility of strategies to shield the vulnerable during the COVID-19 outbreak: a qualitative study in six Sudanese communities 91%
- Disparities in COVID-19 Hospitalizations and Mortality among Black and Hispanic Patients: Cross-Sectional Analysis from the Greater Houston Metropolitan Area 91%
- Variation in real world dementia risk profiles in 6171 adults drawn from the Australian CogDrisk website according to demographic characteristics 91%
Similar papers in this journal
- COVID-19 vaccine acceptance in older Syrian refugees: preliminary findings from an ongoing study 90%
- Characteristics associated with COVID-19 vaccination status among staff and faculty of a large, diverse University in Los Angeles 90%
- Sociodemographic factors and self-restraint from social behaviors during the COVID-19 pandemic in Japan: a cross-sectional study 89%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.