Item and scale properties of the Oral Health Literacy Adults Questionnaire assessed by item response theory

Pattanaik, Swaha; John, Mike T.; Kohli, Nidhi; Davison, Mark L.; Chung, Shu-Hsing; Self, Karl; Naik, Amruta; Flynn, Patricia M.

doi:10.1111/jphd.12434

Cited by 5 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Russian [323]; Chilean [324]; Malaysian [325 Thai [328]; Australian [329]; Brazilian [330,331] He LD-14 [332] Naghibi Sistani et al [121] Oral health literacy for Adults Questionnaire (OHL-AQ) American [333,334]; Persian [335]; Hindi [336]; Mandarin [337] -Shreffler-Grant et al [123] Montana State University (MSU) CAM Health Literacy Scale American [338] -O'Connor et al [125] Mental Health Literacy Scale (MHLS)…”

Section: Content Specific Health Literacy Instrumentsmentioning

confidence: 99%

Measuring health literacy: A systematic review and bibliometric analysis of instruments from 1993 to 2021

et al. 2022

View full text Add to dashboard Cite

Background It has been about 30 years since the first health literacy instrument was developed. This study aimed to review all existing instruments to summarize the current knowledge on the development of existing measurement instruments and their possible translation and validation in other languages different from the original languages. Methods The review was conducted using PubMed, Web of Science, Scopus, and Google Scholar on all published papers on health literacy instrument development and psychometric properties in English biomedical journals from 1993 to the end of 2021. Results The findings were summarized and synthesized on several headings, including general instruments, condition specific health literacy instruments (disease & content), population- specific instruments, and electronic health. Overall, 4848 citations were retrieved. After removing duplicates (n = 2336) and non-related papers (n = 2175), 361 studies (162 papers introducing an instrument and 199 papers reporting translation and psychometric properties of an original instrument) were selected for the final review. The original instruments included 39 general health literacy instruments, 90 condition specific (disease or content) health literacy instruments, 22 population- specific instruments, and 11 electronic health literacy instruments. Almost all papers reported reliability and validity, and the findings indicated that most existing health literacy instruments benefit from some relatively good psychometric properties. Conclusion This review highlighted that there were more than enough instruments for measuring health literacy. In addition, we found that a number of instruments did not report psychometric properties sufficiently. However, evidence suggest that well developed instruments and those reported adequate measures of validation could be helpful if appropriately selected based on objectives of a given study. Perhaps an authorized institution such as World Health Organization should take responsibility and provide a clear guideline for measuring health literacy as appropriate.

show abstract

Section: Content Specific Health Literacy Instrumentsmentioning

confidence: 99%

Measuring health literacy: A systematic review and bibliometric analysis of instruments from 1993 to 2021

et al. 2022

View full text Add to dashboard Cite

show abstract

“…As an effective way to investigate oral health status, oral health surveys can determine the frequency, intensity, and spread of oral diseases, such as oral behaviors, oral health cognition, and quality of life in a particular time frame [ 2 ]. On the basis of the analysis and mining of oral health surveys, we can obtain deeper insights into the oral health status of individuals, understand oral diseases, and identify their impacting factors.…”

Section: Introductionmentioning

confidence: 99%

Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study

Xu¹,

Ma²,

Pan³

et al. 2023

JMIR Med Inform

View full text Add to dashboard Cite

Background Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys. Objective We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. Methods First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. Results A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health conditions. Conclusions A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases.

show abstract

Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study (Preprint)

Xu¹,

Ma²,

Pan³

et al. 2023

Preprint

View full text Add to dashboard Cite

BACKGROUND Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is quite a difficult and complicated task to gain insights from multidimensional oral health surveys. OBJECTIVE In this paper, we develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. METHODS First, diseases and groups are embedded into data portraits based on their multidimensional attributes. Based on group classification, correlation patterns are then built for diseases, behaviors, symptoms and cognition to reveal their correlation features. Given the extricated knowledge of diseases, groups, behaviors and their attributes, a knowledge graph is further constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. RESULTS A set of meaningful user interactions are integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system. CONCLUSIONS In this paper, we propose a visualization framework for multidimensional oral health surveys. A series of user-friendly interactions are integrated to propose a visual analysis system that can help users further explore the regulations of oral health conditions. Case studies based on real-world datasets demonstrate the effectiveness of our system in the exploration of oral diseases.

show abstract

Item and scale properties of the Oral Health Literacy Adults Questionnaire assessed by item response theory

Cited by 5 publications

References 22 publications

Measuring health literacy: A systematic review and bibliometric analysis of instruments from 1993 to 2021

Measuring health literacy: A systematic review and bibliometric analysis of instruments from 1993 to 2021

Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study

Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study (Preprint)

Contact Info

Product

Resources

About