This research aims to gain an in-depth understanding of precariously housed women's experiences related to health and access to health care during the COVID-19 pandemic using a grounded theory approach. Qualitative data were obtained through interviews with 17 precariously housed women from Izmir, Turkey. Poor health among most participants was primarily attributed to unfavorable living conditions and weakened community networks. The COVID-19 pandemic exacerbated existing health issues due to barriers in accessing basic needs. Food insecurity was widespread during the pandemic and the critical role of aid and the inadequacy of social assistance in securing food were emphasized. Women's health perceptions were significantly shaped by gender, and gendered caregiving duties have restricted women's healthcare access. Access to healthcare was also limited by financial challenges, with health insurance being a crucial determinant. Longer waiting times, often exacerbated by the appointment system, and language were significant barriers to healthcare access. The findings propose that the participants were precarized by the blindness of COVID-19 measures to vulnerabilities, which resulted in deeper inequalities in housing, food, employment, and healthcare access. This research addresses the political, commercial, and social determinants of precariously housed women's health. Improving precariously housed women's health and wellbeing requires implementation of public policies targeting to improve housing quality, provide targeted assistance to food insecurity, promote gender inclusiveness, and foster gender empowerment.
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http://dx.doi.org/10.1177/00469580241246478 | DOI Listing |
J Eval Clin Pract
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School of Primary and Allied Health Care, Monash University, Melbourne, Australia.
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View Article and Find Full Text PDFFront Public Health
January 2025
Psychiatric University Clinic of Charité at St. Hedwig Hospital, Department of Psychiatry and Neurosciences, Berlin, Germany.
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View Article and Find Full Text PDFJ Multimorb Comorb
January 2025
Department of Health Systems and Policy, Kamuzu University of Health Sciences, Blantyre, Malawi.
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View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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