The Visible Behaviour of Drowning Persons: A Pilot Observational Study Using Analytic Software and a Nominal Group Technique

Nurses' perspectives on user-friendly self-sampling interventions for diagnosis of sexually transmitted infections among young women in eThekwini district municipality: a nominal group technique

BACKGROUND

Syndromic management in the main non-laboratory-based management approach for sexually transmitted infections (STI) in most low- and middle-income countries (LMICs) but it has limitations. Self-sampling has been proven as a suitable alternative approach to help improve management STIs by improving access to diagnosis among vulnerable populations. We sought to determine health workers' perspectives on user-friendly self-sampling interventions for STIs among young women in eThekwini District Municipality.

METHODS

Healthcare workers providing STI healthcare services in the study location participated in a nominal group technique (NGT) workshop. The NGT workshop was aimed enabling collaboration with key health providers in identifying user-friendly self-sampling interventions for diagnosis of STIs among young women. Data collection was conducted in two phases: phase 1 determined barrier that hinder young women from accessing current STI healthcare services and phase 2 focused on determining the key strategies for self-sampling interventions to diagnose STIs in young women. Thematic analysis and percentage form analysis were used to examine qualitative and quantitative data respectively.

RESULTS

The following barriers were identified: negligence; myths about STIs; fear of judgement; denial; operating hours; lack of knowledge of STI symptoms and safe sex practices; and stigma associated with STIs. The following strategies were suggested: hand out self-sampling kits at popular restaurants; collect self-sampling kits from security guard at primary healthcare clinics (PHCs); receive STI diagnostic results via SMS or email or the clinic for treatment; improve youth friendly services at PHCs; educate the public on proper use of the kits. Education about STIs and handing out self-sampling kits at clinics, universities, schools, pharmacies or via outreach teams were ranked high priority strategies.

CONCLUSIONS

The findings highlight the need to address stigma and fear of judgment and provide comprehensive education to improve healthcare-seeking behaviour in young women. Additionally, the study also indicates that using eHealth solutions could significantly enhance the accessibility and efficiency of STI healthcare services in LMICs.

An expert-novice comparison of lifeguard specific vigilance performance

INTRODUCTION

Lifeguards must maintain alertness and monitor an aquatic space across extended periods. However, lifeguard research has yet to investigate a lifeguard's ability to maintain performance over time and whether this is influenced by years of certified experience or the detection difficulty of a drowning incident. The aim of this study was to examine whether lifeguard experience, drowning duration, bather number, and time on task influences drowning detection performance.

METHOD

A total of 30 participants took part in nine 60-minute lifeguard specific tasks that included 11 drowning events occurring at five-minute intervals. Each task had manipulated conditions that acted as the independent variables, including bather number and drowning duration.

RESULTS

The experienced group detected a greater number of drowning events per task, compared to novice and naïve groups. Findings further highlighted that time, bather number, and drowning duration has a substantial influence on lifeguard specific drowning detection performance.

PRACTICAL APPLICATIONS

It is hoped that the outcome of the study will have applied application in highlighting the critical need for lifeguard organizations to be aware of a lifeguard's capacity to sustain attention, and for researchers to explore methods for minimizing any decrement in vigilance performance.

Surfers as aquatics rescuers in Portugal and Spain: Characteristics of rescues and resuscitation knowledge

The aim of this study was to analyze the rescues carried out by surfers from Portugal and Spain, their knowledge of rescue and resuscitation and their perception and risk behavior while surfing. An online survey was conducted in 2048 surfers from Portugal and Spain, with questions regarding the demographic characteristics, experience, perception and risk behavior of the surfers; rescues attended by the surfers and surfer's knowledge and experience in rescue and resuscitation. Concerning the number of rescues carried out by surfers, 78.5% of the participants had to carry out at least one rescue in their lifetime. A significant association was found between the years of surfing experience, the surfing level and the number of rescues carried out (p < 0.05). Thirty-five-point eight percent of the surfers never attended a cardiopulmonary resuscitation (CPR) course and 76.2% had no work experience as a lifeguard. Correspondingly, the vast majority of the surfers analyzed did not have the essential knowledge about rescue and resuscitation. This study provides evidence of the important role that surfers play in saving lives on Portuguese and Spanish beaches. The results suggest that the number of rescues conducted by surfers each year in Portugal and Spain is relevant to reducing the number of fatalities that occur along coasts.

Deep Learning and 5G and Beyond for Child Drowning Prevention in Swimming Pools

Drowning is a major health issue worldwide. The World Health Organization's global report on drowning states that the highest rates of drowning deaths occur among children aged 1-4 years, followed by children aged 5-9 years. Young children can drown silently in as little as 25 s, even in the shallow end or in a baby pool. The report also identifies that the main risk factor for children drowning is the lack of or inadequate supervision. Therefore, in this paper, we propose a novel 5G and beyond child drowning prevention system based on deep learning that detects and classifies distractions of inattentive parents or caregivers and alerts them to focus on active child supervision in swimming pools. In this proposal, we have generated our own dataset, which consists of images of parents/caregivers watching the children or being distracted. The proposed model can successfully perform a seven-class classification with very high accuracies (98%, 94%, and 90% for each model, respectively). ResNet-50, compared with the other models, performs better classifications for most classes.

Safety optimization in an accident-prone aquatic context: A qualitative study of drowning risk detection by public pool lifeguards

INTRODUCTION

Drownings occur in French public swimming pools (PCP) in spite ofconstant supervision by qualified lifeguards. This studyhypothesizesthat organizational aspects may affect the mission of pool supervision andtakes a systemic approach that views safety as an emergent property, and drowning as a multifactorial and long-term process that is not restricted to the end of an accidental sequence.

METHOD

We conducted a qualitative survey at four municipal poolsin France based on 30 semi-structured interviews.

RESULTS

The findings revealed that several interrelated elements influence the detection by lifeguards of bathers in distress: (a) their training, their conception of the profession, and the reasons that led them to enter this occupation; (b) their representations of the drowning risk and the evaluation of their ability to perceive such situations; (c) the question of regulation; and (d) the methods of dealing with this framework in daily operations, which involves trade-offs and appropriations at each level of the system.

CONCLUSIONS

In order to identify the most significant influences, it seems relevant:(a) to shift the focus away from these professionals; and (b) to prioritize an analysis of the functioning of the risk management system as a whole, not just critical or post-accident periods. In order to reduce the risk to swimmers, it seems preferable to identify the source of the constraints that weigh daily on lifeguards in charge of pool supervision.The functioning of PSP's is the end result of joint regulation processes likely to influence, positively or negatively, the identified feedback loops. An analysis of the pool supervision can help to identify early warning signs of vulnerability (which can sometimes be acted upon at low cost),identify the practical implications, and makepreventive recommendations.

The experience of drowning

Internationally, drowning is a leading cause of accidental death that features in many legal cases. In these cases, possible mitigations and the ‘pain and suffering’ in terms of the duration and subjective experience of drowning are often pivotal in determining levels of compensation and outcome. As a result, there is a requirement to understand the stages of the drowning process, and the duration and physiological and subjective responses associated with each stage. In this short review we focus on these issues.

Automated and Intelligent System for Monitoring Swimming Pool Safety Based on the IoT and Transfer Learning

Recently, integrating the Internet of Things (IoT) and computer vision has been utilized in swimming pool automated surveillance systems. Several studies have been proposed to overcome off-time surveillance drowning incidents based on using a sequence of videos to track human motion and position. This paper proposes an efficient and reliable detection system that utilizes a single image to detect and classify drowning objects, to prevent drowning incidents. The proposed system utilizes the IoT and transfer learning to provide an intelligent and automated solution for off-time monitoring swimming pool safety. In addition, a specialized transfer-learning-based model utilizing a model pretrained on “ImageNet”, which can extract the most useful and complex features of the captured image to differentiate between humans, animals, and other objects, has been proposed. The proposed system aims to reduce human intervention by processing and sending the classification results to the owner’s mobile device. The performance of the specialized model is evaluated by using a prototype experiment that achieves higher accuracy, sensitivity, and precision, as compared to other deep learning algorithms.