Purpose: Although falls often result in serious injury among seniors residing in long-term care (LTC), there is a paucity of research about LTC staff perceptions about falls. Our purpose was to elicit opinions of LTC staff about falls and fall prevention given 'least restraint' policies. We also aimed to identify obstacles for optimal falls prevention.
Method: Data were collected from administrators and a wide variety clinical staff (N = 98; 7 LTC facilities) using 11 focus groups and 28 interviews. Questions were asked about clinical practices related to falls. We employed thematic analysis to ascertain primary and secondary themes within the data.
Results: Participants viewed falls as a major challenge. They expressed concerns about their ability to control falls and manage consequences. Participants were conflicted about the role of restraints in falls management. Although they acknowledged beneficial effects of least restraint in terms of resident independence and increased activity, they also noted that in some instances, restraints may prevent falls, especially when individuals with dementia are considered.
Conclusions: Participants were highly attentive to issues surrounding falls. However, many were unaware of clinically important findings from relevant research and misperceived fall-related (restraint) policies. Physical therapists have a role to play in education initiatives targeting these areas.
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http://dx.doi.org/10.3109/09638288.2010.498555 | DOI Listing |
Otol Neurotol
February 2025
Department of Otolaryngology-Head and Neck Surgery.
Objective: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.
Study Design: Prospective, double-blinded, observational study of fall risk scores between trained observers and those of IMU-HAs.
Setting: Tertiary referral center.
PLoS One
January 2025
Department of Pediatrics, China Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
Introduction: Short stature is a frequent complication of DMD, and its pathomechanisms and influencing factors are specific to this disease and the idiosyncratic treatment for DMD.
Purpose: To establish the height growth curve of early DMD, and evaluate the potential influencing markers on height growth, provide further evidence for pathological mechanism, height growth management and bone health in DMD.
Methods: A retrospective, cross-sectional study of 348 participants with DMD aged 2-12 years was conducted at West China Second Hospital of Sichuan University from January 2023 to October 2023.
J Chin Med Assoc
November 2024
Division of Trauma Surgery, Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC.
Background: Trauma is consistently among the top ten causes of death worldwide. The aging population, constituting 15.21% of adults aged over 65 in Taiwan as of November 2019, has significantly impacted healthcare expenditures.
View Article and Find Full Text PDFJ Cancer Res Ther
December 2024
Department of Colorectal Surgery, Shanghai Cancer Center, Fudan University, Xuhui District, Shanghai, China.
Objective: Carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) serve as pivotal tumor markers in colorectal cancer (CRC). However, uncertainty persists regarding the prognostic significance of the two tumor markers when falling within the normal range. We attempt to compare the prognostic differences of tumor markers at different levels within the reference range.
View Article and Find Full Text PDFJ Occup Health
January 2025
Panasonic Corporation, Department Electric Works Company/Engineering Division, Osaka, Japan.
Background: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers.
Methods: Train data (n=190, age 54.
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