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Background: Direct-access physiotherapy practice has led to a global review of the use of differential-diagnostic modalities such as musculoskeletal imaging (MI) in physiotherapy.
Objective: To explore the MI authority, levels of training, attitude, utilisation, and competence among clinical physiotherapists in Nigeria.
Methods: This national cross-sectional study analysed a voluntary response sample of 400 Nigerian physiotherapists that completed the online version of the Physiotherapist's Musculoskeletal Imaging Profiling Questionnaire (PMIPQ), using descriptive statistics, Spearman's correlation, Mann-Whitney U, Kruskal-Wallis, and Friedman's ANOVA tests.
Results: Of the 400 participants, 93.2% believed that physiotherapists should use MI in clinical practice. However, only 79.8% reported having MI authority in their practice settings. The participants' median (interquartile range) levels of training =10 (24) and competence =16 (24) were moderate. Nonetheless, levels of training (χ2 [15] = 1285.899, p = 0.001), and competence (χ2 [15] = 1310.769, p < 0.001) differed across MI procedures. The level of training and competence in x-ray referral and utilisation was significantly higher than magnetic resonance imaging, computed tomography scan, ultrasonography, scintigraphy, and dual-energy x-ray absorptiometry, in that order (p < 0.05). There was a significant positive correlation between the levels of training and competence (rho =0.61, p < 0.001). The participants had a positive attitude =32 (32) and occasionally used MI in clinical practice =21 (28).
Conclusion: Majority of the respondents believed they had MI authority although there was no explicit affirmation of MI authority in the Nigerian Physiotherapy Practice Act. Participants had a positive attitude towards MI. However, levels of MI training, competence, and utilisation were moderate. Our findings have legislative and curriculum implications.
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http://dx.doi.org/10.1186/s12909-022-03769-x | DOI Listing |
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Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Cognitive function is a critical health indicator of older adults in later life. However, previous research has paid less attention to the impact of pre-retirement work-related characteristics on cognitive functions, especially in Asia. Thus, this study aims to examine the relationship between work-related factors and cognitive functions of the retired population, using Taiwan as an example.
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School of Chemistry, Engineering Research Center of Energy Storage Materials and Devices, Ministry of Education, National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Engineering Resea
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Department of Sport, Exercise and Health, Sports and Exercise Medicine, Medical Faculty, University of Basel, Basel, Switzerland.
The current guidelines for cardiovascular disease prevention by the European Society of Cardiology highlight the undisputable benefits of exercise and a physically active lifestyle for cardiovascular risk reduction. In addition to the health benefits of physical activity, observational data suggests that regular physical activity lowers all-cause mortality. However, this was not confirmed by Mendelian randomization studies and randomized controlled trials.
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Research Institute for Korean Medicine, Pusan National University, Yangsan 50612; Department of Korean Medical Science, School of Korean Medicine, Pusan National University, Yangsan 05612, Korea.
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Computer Science and Business Systems, Sri Krishna College of Engineering and Technology, Coimbatore, India.
Subject-independent emotion detection using EEG (Electroencephalography) using Vibrational Mode Decomposition and deep learning is made possible by the scarcity of labelled EEG datasets encompassing a variety of emotions. Labelled EEG data collection over a wide range of emotional states from a broad and varied population is challenging and resource-intensive. As a result, models trained on small or biased datasets may fail to generalize well to unknown individuals or emotional states, resulting in lower accuracy and robustness in real-world applications.
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