Safe Learning Research Articles

Quality improvement initiative: implementing and redefining video review of real-time neonatal procedures using action research

Video review (VR) of procedures in the medical environment can be used to drive quality improvement. However, first it has to be implemented in a safe and effective way. Our...

The Need for a Chainsaw Safety Training Program for Female Forest Landowners

Female forest landowners (FFLO) are increasing in numbers but have been marginalized in technical training programs in the past. We conducted chainsaw safety training programs geared towards FFLO and compared program evaluation results with results from male-dominated chainsaw training workshops. FFLO are limited in their technical knowledge at the beginning of a workshop, are more likely to own different types of chainsaws than male participants, and generally liked having a women-only workshop. The takeaway is that FFLO strive in a women-only environment and that more women-focused extension training programs are needed to provide with a safe and inclusive learning environment.

DePAint: a decentralized safe multi-agent reinforcement learning algorithm considering peak and average constraints

DePAint: a decentralized safe multi-agent reinforcement learning algorithm considering peak and average constraints

Early grade teachers’ professional development programs for promoting school safety: Does it make a difference?

This study explored the influence of training on helping teachers to promote greater school safety. A qualitative case study design was adopted, and 213 early grade teachers from Tanzania Mainland and Zanzibar took part in the study. Data were gathered through individual semi-structured interviews before being thematically analysed. It was evident that teachers were inadequately prepared in terms of autonomy and supportive communication to help their pupils develop crucial skills for creating a safe learning environment. In- service professional development programs related to social and emotional learning would enhance teachers’ ability to support their pupils to address school safety concerns.

The Privacy Price of School Safety Stakeholders' Perceptions Towards the Use of Closed-Circuit Television (CCTV) in Schools

CCTV surveillance cameras have played an influential role in safeguarding security, discipline, and a conducive learning environment in Nepali schools. However, as surveillance technology is gradually being installed and used, there is a growing concern about privacy violations. In this context, the article aims to explore the distinct perceptions and understanding of the key stakeholders, namely students, teachers and administrators, regarding using CCTV surveillance technology in their natural setting. This article especially seeks the stakeholders’ perceptions on the impact of surveillance technology on students and teachers about their privacy rights. To meet the objective, I have employed a qualitative study that follows the interpretivists’ research paradigm. I have purposively selected five respondents from each school, incorporating three students and two teaching faculties cum administrative persons. I employed semi-structured interviews at three secondary schools from inner Terai of Nawalpur district. Moreover, I utilized non-participant observation, field notes and google-form as a tool to gather content-rich data and verify the information accumulated through semi-structured interviews, regarding their lived and subjective perceptions about using CCTVs. Findings indicate effectively managing and mitigating various risk factors within schools’ premises, and cultivating a culture of shared responsibilities among students and teachers for building a conducive learning culture in schools. It is also found that surveillance technology in Nepali schools nurtures suspicion among stakeholders, along with acknowledging the importance of comprehensive policy, and students’ privacy concerns to ensure a safe and friendly learning culture.

SARS-CoV-2 wastewater surveillance at two university campuses: lessons learned and insights on intervention strategies for public health guidance

ABSTRACT Wastewater surveillance has been a tool for public health officials throughout the COVID-19 pandemic. Universities established pandemic response committees to facilitate safe learning for students, faculty, and staff. These committees met to analyze both wastewater and clinical data to propose mitigation strategies to limit the spread of COVID-19. This paper reviews the initial efforts of utilizing campus data inclusive of wastewater surveillance for SARS-CoV-2 RNA concentrations, clinical case data from university response teams, and mitigation strategies from Grand Valley State University in West Michigan (population 21,648 students) and Oakland University in East Michigan (population 18,552 students) from November 2020 to April 2022. Wastewater positivity rates for both universities ranged from 32.8 to 46.8%. Peak viral signals for both universities directly corresponded to variant points of entry within the campus populations from 2021 to 2022. It was found that the organization of clinical case data and variability of wastewater testing data were large barriers for both universities to effectively understand disease dynamics within the university population. We review the initial efforts of onboarding wastewater surveillance and provide direction for structuring ongoing surveillance workflows and future epidemic response strategies based on those that led to reduced viral signals in campus wastewater.

Event-Triggered Constrained Optimal Control for Organic Rankine Cycle Systems via Safe Reinforcement Learning.

The organic Rankine cycle (ORC) is an effective application for converting low-grade heat sources into power and is crucial for environmentally friendly production and energy recovery. However, the inherent complexity of the mechanism, its strong and unidentified nonlinearity, and the presence of control constraints severely impair the design of its optimal controller. To solve these issues, this study provides a novel event-triggered (ET) constrained optimal control approach for the ORC systems based on a safe reinforcement learning technique to find the optimal control law. Instead of employing the usual non-quadratic integral form to solve the control-limited optimal control problems, a constraint handling strategy based on a relaxed weighted barrier function (BF) technique is proposed. By adding the BF terms to the original value function, a modified value iteration algorithm is developed to make the control input solutions that tend to violate the constraints be pushed back and maintained in their safe sets. In addition, the ET mechanism proposed in this article is critically required for the ORC systems, and it can significantly reduce the computational load. The combination of these two techniques allows the ORC systems to achieve set-point tracking control and satisfy the control restrictions. The proposed approach is conducted based on a heuristic dynamic programming framework with three neural networks (NNs) involved. The safety and convergence of the proposed approach and the stability of the closed-loop system are analyzed. Simulation results and comparisons are presented to demonstrate its effectiveness.

A Safe Preference Learning Approach for Personalization With Applications to Autonomous Vehicles

A Safe Preference Learning Approach for Personalization With Applications to Autonomous Vehicles

Exploring Medical Student Experiences With Direct Observation During the Pediatric Clerkship.

Direct observation (DO) enables assessment of vital competencies, such as clinical skills. Despite national requirement that medical students experience DOs during each clerkship, the frequency, length, quality, and context of these DOs are not well established. This study examines the quality, quantity, and characteristics of DOs obtained during pediatrics clerkships across multiple institutions. This multimethod study was performed at 6 U.S.-based institutions from March through October 2022. In the qualitative phase, focus groups and/or semistructured interviews were conducted with third-year medical students at the conclusion of pediatrics clerkships. In the quantitative phase, the authors administered an internally developed instrument after focus group discussions or interviews. Qualitative data were analyzed using thematic analysis, and quantitative data were analyzed using anonymous survey responses. Seventy-three medical students participated in 20 focus groups, and 71 (97.3%) completed the survey. The authors identified 7 themes that were organized into key principles: before, during, and after DO. Most students reported their DOs were conducted primarily by residents (62 [87.3%]) rather than attendings (6 [8.4%]) in inpatient settings. Participants reported daily attending observation of clinical reasoning (38 [53.5%]), communication (39 [54.9%]), and presentation skills (58 [81.7%]). One-third reported they were never observed taking a history by an inpatient attending (23 [32.4%]), and one-quarter reported they were never observed performing a physical exam (18 [25.4%]). This study revealed that students are not being assessed for performing vital clinical skills in the inpatient setting by attendings as frequently as previously believed. When observers set expectations, create a safe learning environment, and follow up with actionable feedback, medical students perceive the experience as valuable; however, the DO experience is currently suboptimal. Therefore, a high-quality, competency-based clinical education for medical students is necessary to directly drive future patient care by way of a competent physician workforce.

Optimizing anti-collision strategy for MASS: A safe reinforcement learning approach to improve maritime traffic safety

Maritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management.

Research on College Network Information Security Protection in the Digital Economy Era

In the era of the digital economy, the informatization degree of various industries is getting deeper and deeper, and network information security has also come into people’s eyes. Colleges and universities are in the position of training applied talents, because of the needs of teaching and education, as well as the requirements of teaching reform, the information construction of colleges and universities has been gradually improved, but the problem of network information security is also worth causing people to ponder. The low security of the network environment will cause college network information security leaks, and even hackers will attack the official website of the university and leak the personal information of teachers and students. To solve such problems, this paper studies the protection of college network information security against the background of the digital economy era. This paper first analyzes the significance of network information security protection, then points out the current and moral problems, and finally puts forward specific countermeasures, hoping to create a safe learning environment for teachers and students for reference.

"This smoke will finish us": impacts of cooking with polluting fuels on air quality, health and education in three schools in Nairobi, Kenya

Abstract Links between cooking with polluting fuels (e.g., wood and charcoal), air pollution and health in domestic settings are well-established. However, few studies have been conducted in schools that rely on such fuels for catering. This study is the first investigation of air pollution, cooking, and health in schools in Nairobi, Kenya. We carried out an in-depth mixed-methods study in three schools (two primary schools and a college) in an informal settlement using wood and/or charcoal for catering. In each school, repeated 24-hour air pollution measurements (fine particulate matter (PM2.5) and carbon monoxide (CO)) were collected to assess concentrations in the main kitchen and a nearby classroom, in addition to personal exposure for the main cook. Surveys with catering staff collected data on perspectives on air pollution and health symptoms. Focus groups were conducted with catering staff, teachers and senior management to understand perceived impacts on health and the school environment. 24-hour levels of PM2.5 were found to surpass World Health Organization (WHO) interim target level 1 (IT-1) (35µg/m3) in all schools – with levels three times higher (107.6µg/m3) in classrooms, ten times higher (316.2µg/m3) in kitchens and nearly six times higher (200.9µg/m3) among cooks. Peak levels of pollution were closely linked to times of stove use, as concentrations doubled in classrooms and tripled in kitchens during cooking. Catering staff reported being concerned about their health, and reported experiencing wheezing, chest pains, eye irritation and headaches and attempted to avoid the smoke to reduce exposure. Disturbance to classes from cooking smoke was reported by teachers, with students reporting coughing and sneezing from exposure. Support is needed to enable clean cooking transitions in schools to create a healthy and safe learning environment.

Adaptive Cruise Control Based on Safe Deep Reinforcement Learning.

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback-Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects.

The Role of Native Speaker Peers on Language Learners’ Fear of Negative Evaluation and Language Anxiety

This study investigated graduate-level international students’ linguistic and learning experiences in classrooms predominated by native English-speaking (NES) students. The central objective was to discern the impact of NES students’ presence on the fear of negative evaluation experienced by international students speaking English as a second language in an American university. A survey was initially administered to 22 graduate-level international students, and five international students, distinguished by heightened levels of anxiety associated with negative evaluation in classrooms primarily dominated by NES students, were interviewed. The interview data were analyzed with domain analysis. The findings elucidated that international students’ fear of negative evaluation depended mainly on instructors’ practices and international students’ perceptions of NES peers’ attitudes. Moreover, the increased fear of negative evaluation adversely impacted their linguistic behaviors and overall engagement with instructional content. The findings implied a need for more inclusive and safe learning environments in graduate schools.

“STEPS”: A Simulation Tool to Enhance Psychological Safety

BackgroundCreating and maintaining a psychologically safe and welcoming environment is an essential element in simulation-based teaching. The purpose of this project was to describe a novel tool the authors developed to visually enhance psychological safety. Using constructs related to psychological safety, the tool was designed and named “Simulation Tool to Enhance Psychological Safety” (STEPS). MethodsSTEPS was designed by a core team of simulation education specialists and then evaluated by a convenience sample of prelicensure (n = 5) and advanced practice (n = 3) nursing simulation educator experts. Using open-ended responses, thes reviewers gave feedback and suggestions for improvement. ResultsFindings revealed that STEPS was positively evaluated as a visual aid to promote psychological safety. Reviewers offered suggestions for improving design features such as making it more inclusive of other disciplines and adding inspirational quotes. The reviewers unanimously agreed that STEPS would be a successful adjunct for promoting psychological safety in simulation. ConclusionsSTEPS is an innovative visual aid to promote a psychologically safe learning environment. We plan to use the tool in simulations across nursing programs.

Safe Reinforcement Learning for Arm Manipulation with Constrained Markov Decision Process

Safe Reinforcement Learning for Arm Manipulation with Constrained Markov Decision Process

Autonomous spacecraft collision avoidance with a variable number of space debris based on safe reinforcement learning

The avoidance of multiple space debris collisions by autonomous spacecraft has garnered significant interests worldwide. Applying deep reinforcement learning (DRL) to autonomous spacecraft collision avoidance problems is still difficult because of limitations on constraint satisfaction and environment state perception, even if DRL is a suitable model-free and data-driven framework. In this research, a state-of-the-art penalized proximal policy optimization (P3O) method is applied to address the spacecraft's autonomous collision avoidance problem, which is formalized as a constrained Markov decision process (CMDP). In contrast with traditional DRL methods, P3O promises to satisfy multiple constraints in actual spaceship operations while also facilitating efficient learning in multi-dimensional, continuous state and action spaces. The scalability of the P3O algorithm is enhanced by combining the feature extraction capabilities and variable-length input capabilities of the long short-term memory (LSTM) networks, enabling the P3O to adapt to a variable number of space debris without the need for network retraining. The performance of the proposed method is compared with other five methods through simulation cases, which verifies the superior performance of the proposed method in terms of scalability, energy consumption, collision probability and constraint satisfaction.

Health Literacy, Migration Traumas, Narrative Medicine and the Language Desk. New practices in translingualism and educational processes

Health literacy (HL) is a complex field encompassing a range of public health education and communication activities that are crucial for interacting with the health care system. In a sustainable world, future healthcare must be accessible to all, and we must ensure that people from migrant backgrounds have got the resources to manage their health. Inequalities in immigrants’ health profiles, a sign of ineffective integration policies, could be mitigated by improving HL levels. The aim of the study was to investigate the space given to HL within formal immigrant learning contexts; the study featured a questionnaire that was intended as a probe into some specific needs and discomforts connected with the condition of translingualism and with the related trauma, which may have a negative impact on language learning. Such interweaving offers a different perspective that generates new teaching practices and the creation of a Language Desk that through language, health literacy and narrative medicine promotes equitable, safe and sustainable learning contexts that enhance the experience of translingualism in all its forms.

Non-parametric Gaussian process movement primitive with via-point constraint for effective and safe robot skill learning

Learning from demonstration (LFD) algorithms has been proven to be an effective way to encode basic human skills, such as probabilistic movement primitives (ProMPs). However, such algorithms are based on parametric methods, which means that more effort is required to expand to multi-dimensional and high reproduction accuracy. In this article, a novel non-parametric LFD algorithm is proposed, named Gaussian process movement primitive based on Gaussian mixture regression (GPGR), which has lower computational complexity and high accuracy. We encapsulate the variability of the demonstration set into the prior of Gaussian processes and propose a necessary and sufficient condition to ensure that the generated trajectory can pass the expected via point with 100% probability, which is different from existing ProMPs and their variants. In addition, we propose a novel trajectory obstacle avoidance method. The method allows efficient and safe robot obstacle avoidance by solving for a small number of via points. The effectiveness of the algorithm is validated through a series of experiments on the LASA dataset and the Franka Emika Panda robot.

What's the CATCH? A Participatory Learning Model for Case Review Rounds to Support a Culture of Safety and Quality Improvement in Pediatric Hospital Medicine

To evaluate the effectiveness of a new model, Case Analysis and Translation to Care in Hospital (CATCH), for the review of pediatric inpatient cases when an adverse event or "close call" had occurred. The curricular intervention consisted of an introductory podcast/workshop, mentorship of presenters, and monthly CATCH rounds over sixteen months. The study was conducted with 22 pediatricians at a single tertiary care center. Intervention assessment occurred using participant surveys at multiple intervals: pre/post the intervention, presenter experience (post), physicians-involved and mentors experience (post), and after each CATCH session. Paired t-tests and thematic analysis were used to analyze data. Time required to support the CATCH process was used to assess feasibility. Our overall experience and data revealed a strong preference for the CATCH model, high levels of engagement and satisfaction with CATCH sessions, and positive presenter as well as physicians-involved and mentor experiences. Participants reported that the CATCH model is feasible, engages physicians, promotes a safe learning environment, facilitates awareness of tools for case analysis, and provides opportunities to create "CATCH of the Day" recommendations to support translation of learning to clinical practice. The CATCH model has significant potential to strengthen clinical case rounds in pediatric hospital medicine. Future research is needed to assess the effectiveness of the model at additional sites and across medical specialities.