Cyberstalking in the United Kingdom: an analysis of the ECHO Pilot Survey

An Analysis of the ECHO Pilot SurveyNetwork for Surviving Stalking is internationally recognised as the leading Registered Charity in the United Kingdom dedicated to supporting victims of stalking, free of cost or commercial gain. It aims to provide support to victims, potential victims and others affected by stalking or harassment throughout the UK, to raise awareness of the subject and to provide information about stalking and harassment to professionals, relevant agencies and the public. As we have moved into an age of electronic information and communication, stalkers have found new, more effective and efficient means to perpetrate their malicious acts; stalkers have become Cyberstalkers. Cyberstalking has become somewhat of an epidemic stretching across the globe. Network for Surviving Stalking began to notice that an increasing number of people searching for support were being stalked or harassed online, making the charity concerned as to the prevalence, nature and impact of cyberstalking. The charity commissioned a team of researchers and together developed an online questionnaire to establish answers to these questions. This report provides an analysis of the responses to the questionnaire

Can Patient Safety Incident Reports Be Used to Compare Hospital Safety? Results from a Quantitative Analysis of the English National Reporting and Learning System Data.

BACKGROUND: The National Reporting and Learning System (NRLS) collects reports about patient safety incidents in England. Government regulators use NRLS data to assess the safety of hospitals. This study aims to examine whether annual hospital incident reporting rates can be used as a surrogate indicator of individual hospital safety. Secondly assesses which hospital characteristics are correlated with high incident reporting rates and whether a high reporting hospital is safer than those lower reporting hospitals. Finally, it assesses which health-care professionals report more incidents of patient harm, which report more near miss incidents and what hospital factors encourage reporting. These findings may suggest methods for increasing the utility of reporting systems. METHODS: This study used a mix methods approach for assessing NRLS data. The data were investigated using Pareto analysis and regression models to establish which patients are most vulnerable to reported harm. Hospital factors were correlated with institutional reporting rates over one year to examine what factors influenced reporting. Staff survey findings regarding hospital safety culture were correlated with reported rates of incidents causing harm; no harm and death to understand what barriers influence error disclosure. FINDINGS: 5,879,954 incident reports were collected from acute hospitals over the decade. 70.3% of incidents produced no harm to the patient and 0.9% were judged by the reporter to have caused severe harm or death. Obstetrics and Gynaecology reported the most no harm events [OR 1.61(95%CI: 1.12 to 2.27), p<0.01] and pharmacy was the hospital location where most near-misses were captured [OR 3.03(95%CI: 2.04 to 4.55), p<0.01]. Clinicians were significantly more likely to report death than other staff [OR 3.04(95%CI: 2.43 to 3.80) p<0.01]. A higher ratio of clinicians to beds correlated with reduced rate of harm reported [RR = -1.78(95%Cl: -3.33 to -0.23), p = 0.03]. Litigation claims per bed were significantly negatively associated with incident reports. Patient satisfaction and mortality outcomes were not significantly associated with reporting rates. Staff survey responses revealed that keeping reports confidential, keeping staff informed about incidents and giving feedback on safety initiatives increased reporting rates [r = 0.26 (p<0.01), r = 0.17 (p = 0.04), r = 0.23 (p = 0.01), r = 0.20 (p = 0.02)]. CONCLUSION: The NRLS is the largest patient safety reporting system in the world. This study did not demonstrate many hospital characteristics to significantly influence overall reporting rate. There were no association between size of hospital, number of staff, mortality outcomes or patient satisfaction outcomes and incident reporting rate. The study did show that hospitals where staff reported more incidents had reduced litigation claims and when clinician staffing is increased fewer incidents reporting patient harm are reported, whilst near misses remain the same. Certain specialties report more near misses than others, and doctors report more harm incidents than near misses. Staff survey results showed that open environments and reduced fear of punitive response increases incident reporting. We suggest that reporting rates should not be used to assess hospital safety. Different healthcare professionals focus on different types of safety incidents and focusing on these areas whilst creating a responsive, confidential learning environment will increase staff engagement with error disclosure

Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation

Consumer protection agencies are charged with safeguarding the public from hazardous products, but the thousands of products under their jurisdiction make it challenging to identify and respond to consumer complaints quickly. From the consumer's perspective, online reviews can provide evidence of product defects, but manually sifting through hundreds of reviews is not always feasible. In this paper, we propose a system to mine Amazon.com reviews to identify products that may pose safety or health hazards. Since labeled data for this task are scarce, our approach combines positive unlabeled learning with domain adaptation to train a classifier from consumer complaints submitted to the U.S. Consumer Product Safety Commission. On a validation set of manually annotated Amazon product reviews, we find that our approach results in an absolute F1 score improvement of 8% over the best competing baseline. Furthermore, we apply the classifier to Amazon reviews of known recalled products; the classifier identifies reviews reporting safety hazards prior to the recall date for 45% of the products. This suggests that the system may be able to provide an early warning system to alert consumers to hazardous products before an official recall is announced

Preliminary Exploration of Main Elements for Systematic Classification Development:Case Study of Patient Safety Incidents

Publisher Copyright: © 2022 JMIR Publications Inc.. All Rights Reserved.Background: Currently, there is no holistic theoretical approach available for guiding classification development. On the basis of our recent classification development research in the area of patient safety in health information technology, this focus area would benefit from a more systematic approach. Although some valuable theoretical and methodological approaches have been presented, classification development literature typically is limited to methodological development in a specific domain or is practically oriented. Objective: The main purposes of this study are to fill the methodological gap in classification development research by exploring possible elements of systematic development based on previous literature and to promote sustainable and well-grounded classification outcomes by identifying a set of recommended elements. Specifically, the aim is to answer the following question: what are the main elements for systematic classification development based on research evidence and our use case? Methods: This study applied a qualitative research approach. On the basis of previous literature, preliminary elements for classification development were specified, as follows: defining a concept model, documenting the development process, incorporating multidisciplinary expertise, validating results, and maintaining the classification. The elements were compiled as guiding principles for the research process and tested in the case of patient safety incidents (n=501). Results: The results illustrate classification development based on the chosen elements, with 4 examples of technology-induced errors. Examples from the use case regard usability, system downtime, clinical workflow, and medication section problems. The study results confirm and thus suggest that a more comprehensive and theory-based systematic approach promotes well-grounded classification work by enhancing transparency and possibilities for assessing the development process. Conclusions: We recommend further testing the preliminary main elements presented in this study. The research presented herein could serve as a basis for future work. Our recently developed classification and the use case presented here serve as examples. Data retrieved from, for example, other type of electronic health records and use contexts could refine and validate the suggested methodological approach.Peer reviewe