Translation and psychometric validation of the Persian version of palliative care attitudes scale in cancer patients.

INTRODUCTION: To improve cancer patients' quality of life, palliative care is necessary. The growth of palliative care, along with the assistance of the government and the collaboration of specialists, also relies on the knowledge and attitude of people. In Iran, there is no tool available to gauge patient attitudes about palliative treatment. The Persian version of the Palliative Care Attitude Scale (PCAS-9) was translated and psychometrically validated in this research among cancer patients. METHODS: This methodological study was conducted in two stages: translation stage and psychometric validation stage. The method of translation was based on that proposed by Polit and Yang. Utilizing a qualitative approach, the scale's face and content validity were investigated. 162 cancer patients who required palliative care based on expert diagnosis participated in the confirmatory factor analysis to establish construct validity. Stability and internal consistency provided evidence of reliability. The data was examined using SPSS18 and AMOS. RESULTS: The "Palliative Care Attitudes Scale" translated well across cultures. Validity on both the face and the content was acceptable. Confirmatory factor analysis (CFA) revealed a good fit for the original three-factor structure. The intra-class correlation coefficient (ICC) was equal to 0.89, while the internal consistency (Cronbach's alpha) reliability of the whole scale was equal to 0.77. CONCLUSIONS: Persian version of the "Palliative Care Attitudes Scale" was acceptable and adequate in cancer patients. Using this tool makes it easier to assess how patients feel about receiving palliative care and how well training sessions are working to change patients' views.

Unraveling symptom interplay: a network analysis of procrastination in gifted students.

BACKGROUND: This study explores the intricate web of symptoms experienced by academically gifted high school students, focusing on procrastination, rumination, perfectionism, and cognitive flexibility. The well-being of these gifted adolescents remains a pivotal concern, and understanding the dynamics of these symptoms is vital. METHODS: A diverse sample of 207 academically gifted high school students from Mashhad, Iran, participated in this study. Using convenience sampling, participants from grades 10, 11, and 12 were included, with detailed assessments conducted through questionnaires measuring the mentioned symptoms. RESULTS: Our network analysis uncovers compelling insights into the interplay of these symptoms: Procrastination, though moderately central, exerts significant influence within the network, underscoring its relevance. Cognitive flexibility, while centrally positioned, curiously exhibits a negative influence, potentially serving as a protective factor. Negative perfectionism emerges as the keystone symptom, with both high centrality and a positive influence. Rumination displays substantial centrality and a positive influence, indicating its role in symptom exacerbation. Positive perfectionism, moderately central, lacks direct influence on other symptoms. CONCLUSION: This network analysis provides a nuanced understanding of the relationships among procrastination, rumination, perfectionism, and cognitive flexibility in academically gifted adolescents. Negative perfectionism and cognitive flexibility emerge as critical factors deserving attention in interventions aimed at enhancing the well-being of this unique group. Further research should explore causal relationships to refine targeted interventions.

Multilayer perceptron modeling for social dysfunction prediction based on general health factors in an Iranian women sample.

In the year 2022, this research conducted an in-person study involving 780 single or widowed women, aged between 20 and 70, falling within the bottom three economic deciles and possessing varying levels of education. All participants held educational qualifications below a high school diploma and were beneficiaries of charitable financial support in Khorasan province, Iran. The study aimed to investigate the predictive factors of social dysfunction in this specific demographic. Data collection spanned a 12-month period throughout 2022, with participants completing the GHQ-28 questionnaire during their visits to the charity office. Clinical in-person interviews were also conducted to gather comprehensive data. Data analysis was carried out using IBM SPSS version 27. The research employed a Multilayer Perceptron (MLP) neural network model, considering an extensive set of input factors and covariates. These factors included cognitive functioning, anxiety, depression, age, and education levels. The MLP model exhibited robust performance, achieving high overall accuracy and sensitivity in identifying cases of high social dysfunction. The findings emphasized the significance of cognitive functioning, anxiety, and depression as pivotal predictors of social dysfunction within this specific demographic, while education and age displayed relatively lower importance. The normalized importance scores provided a relative measure of each covariate's impact on the model's predictions. These results furnish valuable insights for the development of targeted interventions and evidence-based policies aimed at addressing social dysfunction and promoting societal well-being among economically disadvantaged, single or widowed women. Notably, the research underscores the potential of MLP modeling in social science research and suggests avenues for further research and refinement to enhance the model's predictive accuracy, particularly for cases of low social dysfunction.