The current study employs a qualitative approach to uncover the perceptions of Iranian adolescents regarding their bodies, with a focus on the psychological components that contribute to their body image perceptions. Data collection conducted using 13 semi-structured focus group discussions with 42 girls and 42 boys (15-18 years). All focus group discussions were audio recorded and transcribed verbatim. Data analysis was done manually using constant comparative analysis according to the Strauss and Corbin analysis method. Based on the participants' statements, four main themes and related categories emerged from data: (1) Beliefs including uncontrollable body, biased opinion of those around, priority of health, spiritual/religious beliefs and affecting future success, (2) Body-evaluation including direct body evaluation and indirect body evaluation, (3) Feelings and emotions including disgust and hatred, frustration, sadness, shame, envy, fear, approval and satisfaction and (4) Behaviors including lifestyle modifications, beautifying and using make up, extreme dieting, physical inactivity, avoidance behaviors, passive behaviors, aggressive behaviors and social isolation. Findings of the current study provide further information regarding cognitive, emotional and behavioral aspects of body image from the perspectives of adolescents in a West-Asian region.
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http://dx.doi.org/10.1007/s10578-021-01235-1 | DOI Listing |
Body Image
December 2024
Department of Clinical Psychological Science, Maastricht University, PO Box 616, Maastricht 6200 MD, the Netherlands.
In recognition of a need to better understand children's body image, this study aimed to explore how young children describe what they appreciate about their body functionality. A total of 381 British children aged four to six years old were interviewed in a brief play-based session. We looked at the absolute number of responses children gave when asked to list all the amazing things they could do with their bodies as well as the range of responses across body functionality domains using a coding rubric.
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December 2024
Hunan University of Chinese Medicine, Changsha, China.
Stroke has become the leading cause of disability in adults worldwide. Early precise rehabilitation intervention is crucial for the recovery of stroke patients, with the key lying in accurately identifying patients' physical characteristics during the rehabilitation phase. Compared to diagnostic techniques such as medical neuroimaging, traditional Chinese medicine(TCM) tongue diagnosis offers good accessibility and ease of application.
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December 2024
Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via Di Grottarossa, 1035-1039, 00189, Rome, Italy.
Objectives: To evaluate liver enhancement and image quality of abdominal CECT examinations acquired with multiple LBW-based contrast medium injection protocols.
Material & Methods: One hundred fifty patients who underwent a clinically indicated CECT examination were prospectively and randomly assigned to one of the following contrast medium injection protocol groups: A, 700 mg iodine(I)/kg of LBW; B, 650 mgI/kg of LBW; and C, 600 mgI/kg of LBW. Liver signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and magnitude of contrast enhancement (ΔHU) were calculated.
Med Biol Eng Comput
December 2024
School of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China.
Finite element human body models (HBMs) are the primary method for predicting human biological responses in vehicle collisions, especially personalized HBMs that allow accounting for diverse populations. Yet, creating personalized HBMs from a single image is a challenging task. This study addresses this challenge by providing a framework for HBM personalization, starting from a single image used to estimate the subject's skin point cloud, the skeletal point cloud, and the relative positions of the skeletons.
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December 2024
Centre for Ore Deposit and Earth Sciences, School of Natural Sciences, University of Tasmania, Hobart, Australia.
Volcanic stratigraphy reconstruction is traditionally based on qualitative facies analysis complemented by geochemical analyses. Here we present a novel technique based on machine learning identification of crystal size distribution to quantitatively fingerprint lavas, shallow intrusions and coarse lava breccias. This technique, based on a simple photograph of a rock (or core) sample, is complementary to existing methods and allows another strategy to identify and compare volcanic rocks for stratigraphic correlation.
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