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Defects in baby cribs and related products can cause injuries and deaths, and they cost manufacturers and distributors millions of dollars in fines and legal fees and even more in losses of sales and brand image. There has been no prior research regarding automated defect discovery from online reviews of baby cribs, and prior safety defect discovery methods designed and calibrated for other industries must be adapted. We aim to determine which words and phrases are indicators of defects in online reviews and whether sentiment analysis is sufficient for automated defect discovery in the baby crib industry. We find that sentiment analysis serves as a useful tool for automated defect discovery in the baby crib industry and create a supplementary set of “smoke terms” that are strong indicators of safety defects in online reviews of baby cribs. Using our term-based scoring method, we observe a 59% improvement in precision and a 60% improvement in recall when compared to the top-performing prior sentiment method. Our findings provide actionable insights into how analysis of online reviews and other social media can improve baby crib quality management techniques. These terms can be used with immediate effect to monitor and more rapidly identify defects and rectify them before injuries or deaths occur. [ABSTRACT FROM AUTHOR]

Self-report instruments can provide useful information as part of a thorough clinical assessment. However, their use in forensic settings can be problematic. The State-Trait Anger Expression Inventory (STAXI) has recently been proposed as an effective instrument for screening and outcome measurement in anger management programs. This study evaluated the effectiveness of this instrument in a sample of both voluntary and court-ordered anger-management clients, all of whom were determined through diagnostic interviews to have significant anger problems. Contrary to findings in nonforensic samples, the STAXI Trait Anger scale identified only about half of the participants as having anger-management problems severe enough to require intervention. Supplemental analysis with two additional scales did not significantly improve sensitivity. In addition to thorough diagnostic interviewing, forensic use of the STAXI (like similar assessment methods) may require additional validity scales to detect denial or socially desirable response patterns. [ABSTRACT FROM AUTHOR]

In this study, we develop a model that assesses product risk using online reviews from Amazon.com. We first identify unique words and phrases capable of identifying hazards. Second, we estimate risk severity using hazard type weights and risk likelihood using total reviews as a proxy for sales volume. In addition, we obtain expert assessments of product hazard risk (risk likelihood and severity) from a sample of high- and low-risk consumer products identified by a computerized risk assessment model we have developed. Third, we assess the validity of our computerized product risk assessment scoring model by utilizing the experts' survey responses. We find that our model is especially consistent with expert judgments of hazard likelihood but not as consistent with expert judgments of hazard severity. This model helps organizations to determine the risk severity, risk likelihood, and overall risk level of a specific product. The model produced by this study is helpful for product safety practitioners in product risk identification, characterization, and mitigation., (© 2023 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)

Background and Objectives: Many older adults adopt equipment to address physical limitations and reduce dependence on others to complete basic activities of daily living. Although a few prior studies have considered injuries associated with assistive devices for older adults, those studies focused on older adults' health and functional risks for injury. There is limited analysis of older adult injuries involving defective or malfunctioning assistive devices., Research Design and Methods: Data from this study are from the National Electronic Surveillance System All Injury Program which collected data on consumer product-related injuries from a probability sample of 66 hospital Emergency Departments across the United States. Data from 30 776 older adult Emergency Department (ED) injury narratives from 2016 to 2020 were coded according to the assistive device involved and whether malfunctioning led to the injury. The study team manually examined all narratives in which the assistive device was coded to have malfunctioned., Results: A total of 10 974 older adult ED cases were treated for 12 488 injuries involving a defective device. Injuries included 4 212 head and neck injuries (eg, concussion), 4 317 trunk injuries (eg, hip fractures), and 3 959 arm or leg injuries (eg, leg fracture). Of these patients, 4 586 were admitted to a hospital ward for further evaluation and treatment. Seventy percent of these patients were injured while using a walker; in contrast, wheelchairs were implicated in only 4% of the above cases. Design flaws were identified in 8 158 cases and part breakage/decoupling incidents in 2 816 cases., Discussion and Implications: Our findings provide evidence that assistive devices are actively involved in older adult injuries. Further research is needed to reduce injuries associated with assistive devices by educating patients and their careproviders about device use and assembly and developing effective methods for informing manufacturers about malfunctioning devices., Competing Interests: None., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Background Inequitable access to high-technology therapeutics may perpetuate inequities in care. We examined the characteristics of US hospitals that did and did not establish left atrial appendage occlusion (LAAO) programs, the patient populations those hospitals served, and the associations between zip code-level racial, ethnic, and socioeconomic composition and rates of LAAO among Medicare beneficiaries living within large metropolitan areas with LAAO programs. Methods and Results We conducted cross-sectional analyses of Medicare fee-for-service claims for beneficiaries aged 66 years or older between 2016 and 2019. We identified hospitals establishing LAAO programs during the study period. We used generalized linear mixed models to measure the association between zip code-level racial, ethnic, and socioeconomic composition and age-adjusted rates of LAAO in the most populous 25 metropolitan areas with LAAO sites. During the study period, 507 candidate hospitals started LAAO programs, and 745 candidate hospitals did not. Most new LAAO programs opened in metropolitan areas (97.4%). Compared with non-LAAO centers, LAAO centers treated patients with higher median household incomes (difference of $913 [95% CI, $197-$1629], P =0.01). Zip code-level rates of LAAO procedures per 100 000 Medicare beneficiaries in large metropolitan areas were 0.34% (95% CI, 0.33%-0.35%) lower for each $1000 zip code-level decrease in median household income. After adjustment for socioeconomic markers, age, and clinical comorbidities, LAAO rates were lower in zip codes with higher proportions of Black or Hispanic patients. Conclusions Growth in LAAO programs in the United States had been concentrated in metropolitan areas. LAAO centers treated wealthier patient populations in hospitals without LAAO programs. Within major metropolitan areas with LAAO programs, zip codes with higher proportions of Black and Hispanic patients and more patients experiencing socioeconomic disadvantage had lower age-adjusted rates of LAAO. Thus, geographic proximity alone may not ensure equitable access to LAAO. Unequal access to LAAO may reflect disparities in referral patterns, rates of diagnosis, and preferences for using novel therapies experienced by racial and ethnic minority groups and patients experiencing socioeconomic disadvantage.

Background: Older adults who have difficulty moving around are commonly advised to adopt mobility-assistive devices to prevent injuries. However, limited evidence exists on the safety of these devices. Existing data sources such as the National Electronic Injury Surveillance System tend to focus on injury description rather than the underlying context, thus providing little to no actionable information regarding the safety of these devices. Although online reviews are often used by consumers to assess the safety of products, prior studies have not explored consumer-reported injuries and safety concerns within online reviews of mobility-assistive devices., Objective: This study aimed to investigate injury types and contexts stemming from the use of mobility-assistive devices, as reported by older adults or their caregivers in online reviews. It not only identified injury severities and mobility-assistive device failure pathways but also shed light on the development of safety information and protocols for these products., Methods: Reviews concerning assistive devices were extracted from the "assistive aid" categories, which are typically intended for older adult use, on Amazon's US website. The extracted reviews were filtered so that only those pertaining to mobility-assistive devices (canes, gait or transfer belts, ramps, walkers or rollators, and wheelchairs or transport chairs) were retained. We conducted large-scale content analysis of these 48,886 retained reviews by coding them according to injury type (no injury, potential future injury, minor injury, and major injury) and injury pathway (device critical component breakage or decoupling; unintended movement; instability; poor, uneven surface handling; and trip hazards). Coding efforts were carried out across 2 separate phases in which the team manually verified all instances coded as minor injury, major injury, or potential future injury and established interrater reliability to validate coding efforts., Results: The content analysis provided a better understanding of the contexts and conditions leading to user injury, as well as the severity of injuries associated with these mobility-assistive devices. Injury pathways-device critical component failures; unintended device movement; poor, uneven surface handling; instability; and trip hazards-were identified for 5 product types (canes, gait and transfer belts, ramps, walkers and rollators, and wheelchairs and transport chairs). Outcomes were normalized per 10,000 posting counts (online reviews) mentioning minor injury, major injury, or potential future injury by product category. Overall, per 10,000 reviews, 240 (2.4%) described mobility-assistive equipment-related user injuries, whereas 2318 (23.18%) revealed potential future injuries., Conclusions: This study highlights mobility-assistive device injury contexts and severities, suggesting that consumers who posted online reviews attribute most serious injuries to a defective item, rather than user misuse. It implies that many mobility-assistive device injuries may be preventable through patient and caregiver education on how to evaluate new and existing equipment for risk of potential future injury., (©Namrata Mali, Felipe Restrepo, Alan Abrahams, Laura Sands, David M Goldberg, Richard Gruss, Nohel Zaman, Wendy Shields, Elise Omaki, Johnathon Ehsani, Peter Ractham, Laddawan Kaewkitipong. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.03.2023.)

The uncertainty in the timing and severity of disaster events makes the long-term planning of mitigation and recovery actions both critical and extremely difficult. Planners often use expected values for hazard occurrences, leaving communities vulnerable to worse-than-usual and even so-called "black swan" events. This research models disasters in terms of their best-case, most-likely, and worst-case damage estimates. These values are then embedded in a fuzzy goal programming model to provide community planners and stakeholders with the ability to strategize for any range of events from best-case to worst-case by adjusting goal weights. Examples are given illustrating the modeling approach, and an analysis is provided to illustrate how planners might use the model as a planning tool., (© 2021 Society for Risk Analysis.)

Food contamination and food poisoning pose enormous risks to consumers across the world. As discussions of consumer experiences have spread through online media, we propose the use of text mining to rapidly screen online media for mentions of food safety hazards. We compile a large data set of labeled consumer posts spanning two major websites. Utilizing text mining and supervised machine learning, we identify unique words and phrases in online posts that identify consumers' interactions with hazardous food products. We compare our methods to traditional sentiment-based text mining. We assess performance in a high-volume setting, utilizing a data set of over 4 million online reviews. Our methods were 77-90% accurate in top-ranking reviews, while sentiment analysis was just 11-26% accurate. Moreover, we aggregate review-level results to make product-level risk assessments. A panel of 21 food safety experts assessed our model's hazard-flagged products to exhibit substantially higher risk than baseline products. We suggest the use of these tools to profile food items and assess risk, building a postmarket decision support system to identify hazardous food products. Our research contributes to the literature and practice by providing practical and inexpensive means for rapidly monitoring food safety in real time., (© 2020 Society for Risk Analysis.)

Older adults are among the fastest-growing demographic groups in the United States, increasing by over a third this past decade. Consequently, the older adult consumer product market has quickly become a multi-billion-dollar industry in which millions of products are sold every year. However, the rapidly growing market raises the potential for an increasing number of product safety concerns and consumer product-related injuries among older adults. Recent manufacturer and consumer injury prevention efforts have begun to turn towards online reviews, as these provide valuable information from which actionable, timely intelligence can be derived and used to detect safety concerns and prevent injury. The presented dataset contains 1966 curated online product reviews from consumers, equally distributed between safety concerns and non-concerns, pertaining to product categories typically intended for older adults. Identified safety concerns were manually sub-coded across thirteen dimensions designed to capture relevant aspects of the consumer's experience with the purchased product, facilitate the safety concern identification and sub-classification process, and serve as a gold-standard, balanced dataset for text classifier learning., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Introduction: Ensuring occupational health and safety is an enormous concern for organizations, as accidents not only harm workers but also result in financial losses. Analysis of accident data has the potential to reveal insights that may improve capabilities to mitigate future accidents. However, because accident data are often transcribed textually, analyzing these narratives proves difficult. This study contributes to a recent stream of literature utilizing machine learning to automatically label accident narratives, converting them into more easily analyzable fields., Method: First, a large dataset of accident narratives in which workers were injured is collected from the U.S. Occupational Safety and Health Administration (OSHA). Word embeddings-based text mining is implemented; compared to past works, this methodology offers excellent performance. Second, to improve the richness of analyses, each record is assessed across five dimensions. The machine learning models provide classifications of body part(s) injured, the source of the injury, the type of event causing the injury, whether a hospitalization occurred, and whether an amputation occurred. Finally, demonstrating generalizability, the trained models are deployed to analyze two additional datasets of accident narratives in the construction industry and the mining and metals industry (transfer learning). Practical Applications: These contributions improve organizations' capacities to rapidly analyze textual accident narratives., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Author. Published by Elsevier Ltd.. All rights reserved.)

Electronic documentation systems have been widely implemented in the healthcare field. These systems have become a critical part of the nursing profession. This research examines how nurses' general computer skills, training, and self-efficacy affect their perceptions of using these systems. A sample of 248 nurses was surveyed to examine their general computer skills, self-efficacy, and training in electronic documentation systems in nursing programs. We propose a model to investigate the extent to which nurses' computer skills, self-efficacy, and training in electronic documentation influence perceptions of using electronic documentation systems in hospitals. The data supports a mediated model in which general computer skills, self-efficacy, and training influence perceived usefulness through perceived ease of use. The significance of these findings was confirmed through structural equation modeling. As the electronic documentation systems are customized for every organization, our findings suggest value in nurses receiving training to learn these specific systems in the workplace or during their internships. Doing so may improve patient outcomes by ensuring that nurses use the systems consistently and effectively.

A published scientific paper is the end-result of a complex interaction between authors, referees, editors and publishers. Each brings to the process a different agenda, and a widely disparate adherence to standards of competence and integrity. Recent internationally recognized instances of major scientific fraud represent the tip of a potentially large iceberg formed as a consequence of the ineptness or delinquency of one or other elements in the chain of surveillance designed to exercise control over the process of scientific publishing. This subjective analysis attempts to explain why and where the regulatory mechanisms that ought to detect and eliminate the publication or the dissemination by other means of poor, erroneous, or frankly fraudulent scientific finds have broken down, and what can be done to fix them.