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This study aims to identify the reasons for unmet health-care needs and related barriers among the Roma population with chronic diseases in Jordan.

A descriptive survey with a cross-sectional design was conducted, involving a sample of 347 Jordanian Roma participants. Data collection was performed using a structured questionnaire based on the Canadian Community Health Survey.

The analysis revealed that within the three categories of reasons for unmet health-care needs (accessibility, availability and acceptability), “Transportation issues” under the accessibility category constitute the most reported reasons: (mean = 90.4%, SD = 22.6%), followed by “Cost” (mean = 89.0%, SD = 26.2%) and “Care not available in the area” (mean = 85.8%, SD = 23.6%). Predictors of unmet health-care needs were being married, having health insurance and self-perception of mental health (OR = 0.215, p = 0.044), (OR = 0.391, p = 0.008) and (OR = 0.302, p = 0.002) respectively.

Unmet health-care needs are highly prevalent among Jordanian Roma, rendering them a vulnerable group susceptible to other diseases. To address this pressing issue, concerted and comprehensive efforts should be made to improve the utilization and accessibility of health-care services within this community. Furthermore, efforts should be made to elevate their social standing and status. facilitate their integration into the broader community.

The purpose of this paper is to explore the Values in Action (VIA) classification of human strengths and virtues by using unsupervised machine learning techniques, specifically topic modeling algorithms, on a sample of X (formerly known as Twitter) posts. This study aims to investigate if and to what extent the structure of posts with the highest positive sentiment, as determined by topic modeling algorithms, aligns with the structure of the VIA classification.

This study uses a sample of X posts as the data set for the analysis. Unsupervised machine learning techniques, specifically topic modeling algorithms, are used to extract and categorize topics from X posts. The sentiment analysis algorithm is used to identify posts with the most positive sentiment. The structure and representation of these positive sentiment posts are then compared with the structure of the VIA classification.

The results of this study reveal a correlation between the structure of posts with the highest positive sentiment, as determined by topic modeling algorithms, and the structure of the VIA classification. This indicates that the topic structures derived from the X posts exhibit similarities to the categorization of character strengths proposed by the VIA classification. The findings of this study provide empirical validation for the VIA classification framework when applied to social media data.

This paper contributes to the literature by using unsupervised machine learning techniques to validate the VIA classification on social media data. The use of these innovative methods adds a novel dimension to the research on character strengths and virtues.