Multi-tiered Approach Research Articles

Compassion Fatigue Among Australian Oncology Nurses Caring for Adult Cancer Patients: Antecedents, Impactand Mediators.

To explore how Australian oncology nurses perceive and experience compassion fatigue when caring for adult cancer patients, how they mitigate compassion fatigue and identify potential interventions to address compassion fatigue. A qualitative, descriptive study. Twenty Australian oncology nurses caring for adult cancer patients were interviewed between August and September 2023. Participants were recruited from a larger, quantitative study. Interviews were conducted virtually, transcribed verbatim and analysed using thematic analysis. Three themes and nine sub-themes were identified. Participants viewed heavy workloads, low staffingand organisational support as contributing factors to compassion fatigue. They described compassion fatigue as a feeling of having nothing left to give, impacting their ability to self-care, deliver quality patient care and maintain relationships. The pride participants felt in their cancer care work helped mitigate compassion fatigue. Self-care strategies, workplace mentoringand support were identified as ways to mitigate compassion fatigue. The work of oncology nurses can contribute to compassion fatigue, which may impact nurses and patient care. Organisations would benefit from supporting staff to engage in self-care activities, professional development and mentoring. Findings support a multi-tiered approach to addressing compassion fatigue among oncology nurses. Organisations can prioritise adequate staffing levels, mentoring opportunities, focused well-being interventions and provide avenues for meaningful recognition. This study adheres to the COREQ reporting guidelines. No patient or public contribution.

Federated Learning for Energy Efficiency in 6G

This paper presents a multi-tier Federated Learning (FL) architecture designed to optimize energy efficiency in 6G, with particular emphasis on compliance with the Network Data Analytics Function (NWDAF) standards defined by 3GPP. Unlike existing FL architectures that often overlook energy efficiency and lack full integration with network functions like NWDAF, our proposed architecture integrates AI-driven strategies across multi layers. This multi-tier approach dynamically adjusts computation and communication rounds, reducing energy consumption while maintaining high model accuracy and network performance. By addressing challenges such as data heterogeneity and personalisation through adaptive training, intelligent routing, and advanced model aggregation, the architecture significantly enhances energy efficiency. Initial simulations, aligned with NWDAF processing requirements, underscore the architecture’s suitability for deployment in 6G , offering a scalable, energy-efficient, and privacy-preserving solution that aligns with industry standards and addresses key challenges in distributed learning.

The Role Of Mental Health Education In Mitigating Pandemic-Related Psychological Distress In Children

Children with pre-existing conditions, such as ADHD and anxiety, are particularly vulnerable to the mental health consequences of the pandemic. Disruptions to routine and social interactions have exacerbated their symptoms, leading to increased stress and depression. This article examines the role of mental health education in schools as a critical tool for mitigating these challenges. Mental health education equips children with essential stress-management skills through evidence-based interventions such as coping skills training, emotional regulation practices, and resilience-building. The article features case studies that detail the effectiveness of programs like the Los Angeles Unified School District's teletherapy initiative, which provided virtual mental health support to over a million students, significantly reducing stress and anxiety levels during the pandemic. Programs such as the Comprehensive School Mental Health Systems (CSMHS) employ a multitiered approach, addressing prevention, early intervention, and support for severe mental health concerns in collaboration with community health professionals. The Positive Behavioral Interventions and Supports (PBIS) framework also highlights successful integration into U.S. school systems, ensuring a supportive environment through behavioral reinforcement and targeted mental health education

General skills amidst the details: alternative learning objectives and a framework of competencies for human anatomy.

The field of anatomy is often seen by nonanatomists as concerned primarily with the tasks of locating, naming, and describing structures; these tasks, in turn, are often assumed to require only lower-order cognitive skills (LOCSs), i.e., the Knowledge or Comprehension levels of Bloom's taxonomy. Many nonanatomists may thus believe that studying anatomy does not develop transferable higher-order cognitive skills. Published lists of anatomy learning objectives (LOs) might reinforce this view by focusing attention on numerous details of specific structures and regions. To explore this issue further, we have analyzed the structure of published peer-reviewed LOs by characterizing their organization (single-tiered or multi-tiered), inclusion of function, use of action verbs, and dependence on or independence of context. Our results suggest that previously published LO lists, despite their value, may not fully showcase opportunities for students to develop higher-order skills. In the hope of stimulating further discussion and scholarship, we present here a two-tiered framework of human anatomy competencies, i.e., generalizable skills beyond straightforward recognition and memorization. This framework, which is intended to be both student-facing and faculty-facing, illustrates how anatomy courses may be reframed as opportunities to think critically and develop sophisticated, professionally relevant skills.NEW & NOTEWORTHY Although skilled anatomists know that anatomy is much more than memorization, nonanatomists are often unsure how to emphasize general skills and problem-solving in their teaching of the subject. Here we show how a multi-tiered approach to defining and assessing learning objectives (LOs) can reframe anatomy courses as more than long lists of structures to remember.

Efficient resource allocation for generative AI workloads in cloud-native infrastructures: A multi-tiered approach

Resource management becomes essential in ensuring that generative AI workloads in cloud-native infrastructures deliver the best results. The architecture described in this article targets such workloads due to their inherent fluctuations in resource usage and the difficulties in scaling them. The proposed framework divides resources into groups to guarantee that applications are given support based on difficulty level. The features of the proposed methodology are the performance assessment of resource distribution effectiveness, taking into account metrics, including latency, throughput, and utilization rates. Furthermore, examples have been provided to support the use of this approach and its efficiency in real-life situations. Based on these, applying the multi-tiered approach to resource management improves the organization's operations performance and minimizes expenses connected with resource provisioning. Such a study also emphasizes the importance of developing flexible and effective resource management tools that can be especially useful in modern generative AI development environments.

Possibilities and Limitations in Substituting anti-Drug Antibody Titers with Signal-to-Noise Ratios: A Comprehensive Comparison Using Two Clinical Trial Datasets of Adalimumab.

Immunogenicity assessment is vital in clinical trials and is measured through a multi-tiered approach (screening, confirmatory and titer assays). However, recent studies have suggested that titer results could be reported from ADA signal-to-noise ratios (S/N ratios=sample mean signal/negative control mean signal). More data analysis using two clinical trials of adalimumab: SB5-1003 (single-dose, healthy participants) and SB5-4001 (multiple-dose, interchangeability study, patients with plaque psoriasis), therefore, is indispensable whether substituting ADA S/N ratio as an alternative way of reporting titer results has no impact on interpretation on clinical outcome. In this study, we demonstrated that there is a strong correlation between S/N ratios and titers and no impact on overall PK results. Nonetheless, sub-analyses with time or adalimumab level showed a change in the regression between S/N ratios and titers, leading to different titer values from the same S/N ratio. These data demonstrate that S/N ratios may fully replace titers in limited circumstances such as a biosimilar study which goal is to prove equivalence between the originator and candidate product, but need a caution in other cases.

Robust Routing Optimization in VANET Communication Based on Bellman–Ford Multitier Metaheuristic Algorithms

ABSTRACTIn modern cities, vehicle ad hoc networks (VANETs) hold great promise for improving safety on the roads, traffic control, and communicating. The constantly changing and intricate nature of VANETs hampers adequate routing due to a variety of factors. The work examines the use of a multitiered metaheuristic algorithm that combines Bellman–Ford routing with the Fire Hawk, Gannet Optimization, and Mayfly Optimization algorithms to find optimal routing solutions for VANETs. The suggested work addresses network topological dynamics, actual time limitations, flexibility, connectivity problems, and security issues, in addition to the different quality of service (QoS) requirements in VANETs. Bellman–Ford serves as the foundational routing layer and maintains communication, whereas Fire Hawk, Gannet Optimization, and Mayfly Optimization improve and optimize routing patterns at subsequent levels. The multitier approach aims to strike a balance between the rational and probabilistic aspects of routing so as to satisfy the minimal latency needs of applications that are safety‐critical and respond quickly to the rapid changes in VANETs. The suggested work is evaluated with a traffic density ranging from 500 to 5000 with a step size of 500, and it has been observed that for the highest traffic density of 5000, the proposed work obtains a latency of 3.88 ms, data delivery rate of 0.944, a throughput of 2.94 Mbps, and energy efficiency of 97.14%. The proposed work remains robust compared to integrating state‐of‐the‐art metaheuristic algorithms such as ECRA, HOPRP, and CM. Overall, the proposed routing techniques increase routing efficiency in VANETs and make it possible to create vehicle communication networks that are more trustworthy and safe.

Driving policy changes: RISE-Vac lessons learnt and recommendations

Abstract The RISE-Vac project consortium applied evidence-based public health methods to collate, assess, and analyze all the evidence generated through project activities, including literature review and prospective data collection on quantitative and qualitative aspects of vaccine services implementation in prison settings. A multi-sectorial and inter-disciplinary group of experts was set-up including health professionals, statisticians, social scientists and people with lived experience from civil society organisations, health services, prison administrations, academia and international agencies. A set of recommendations, advice, and policy options on how to increase vaccine uptake, targeted to people in prison and using a citizen-centred approach, were developed and tailored to specific needs profiles of different prison population subgroups. Vaccinations to be considered in prison settings were grouped according to target population and/or public health objective into: Seasonal vaccinations and vaccination during outbreak; Childhood life-course vaccinations (booster doses); Vaccinations for cancer prevention; Vaccination for older population groups and people with chronic conditions. In consideration of the diversity of the countries participating in the project, their epidemiological context and available resources and infrastructures, a multi-tiered approach for the development and implementation of models for vaccination services implementation in prison settings was devised, encompassing core vaccinations, such as HBV, Flu, COVID-19 and tetanus, as well as expanded vaccination offer to respond to enhanced health needs and to promote equity of health outcomes, including e.g. HPV vaccination.

Mapping a food donation supply chain and the implications for social policies in Brazil

PurposeThe coronavirus disease 2019 (Covid-19) pandemic raised global alarms about hunger and food insecurity worldwide and the corresponding need for public policies, particularly in emerging countries. One possible solution for addressing this important topic is a food donation supply chain, which comprises food banks and involves their relationships with suppliers (donors), customers (people in need) and other potential stakeholders. The aim of this research is to understand how different agents in a food donation supply chain use different but interrelated capital dimensions (human, structural and relational) to leverage their resources and build knowledge in their relationships with other stakeholders.Design/methodology/approachWe conducted an abductive single case study with the food bank as a focal organisation in a direct supply chain. Data were collected by way of 15 semi-structured interviews with representatives from each tier in the chain (food bank, donor, social impact company and social movement), non-participant observation and secondary data (e.g. codes of practice, websites and media). All data sources were analysed individually and then cross-checked.FindingsOur findings show that tangible dimensions, such as those that are structural, are present upstream while intangible dimensions – those that are relational and human, for example – are prevalent downstream. The political dimension was identified as an important mechanism that allows stakeholder engagement and access to resources, funding and government incentives.Originality/valueBy applying a multi-tier approach, the present research explores how different stages in this supply chain use intellectual capital (IC) to better manage relationships. The heterogeneity of agents within the food donation supply chain (public, private and third sector), each of which has distinct levels of IC dimensions, can share their knowledge to improve efficiency and social protection mechanism policies. Since the focal organisation is a non-profit organisation, this study also contributes to IC theory.

A GIS-MCDA approach to map environmental suitability of Posidonia oceanica meadows as blue nature-based solutions in the Mediterranean eco-region

The growing environmental risks induced by interacting climate and human-induced pressures threaten the survival and growth of marine coastal ecosystems (MCEs) and the ecosystem services they provide. Nature-based solutions (NBS), consisting of ecosystem-based approaches, have emerged as vital tools for climate adaptation and mitigation facing biodiversity loss and societal challenges. Identifying suitable environmental conditions for implementing Blue-NBS in marine coastal areas is a key priority to drive robust and cost-effective nature-based adaptation pathways. This study developed a suitability model for Blue-NBS, with a specific focus on Posidonia oceanica meadows in the Mediterranean Sea under a baseline scenario. GIS-based Multiple Criteria Decision Analysis (MCDA) was applied for data integration and prioritization of different environmental variables in geomorphological (e.g., depth), water quality (WQ) (e.g., salinity), and climatic (e.g., thermal stress) sub-groups. Suitability classes and scores for each variable were determined using statistical distributions, ensuring a data-driven approach to defining environmental suitability. Variables' weights were derived from the Analytic Hierarchy Process (AHP) based on expert judgment and then combined with scores to generate suitability maps for managing Blue-NBS on seagrasses. Depth was found to be the most dominant environmental variable, with shallow areas (e.g., Northern Adriatic, Gulf of Gabés) showing higher suitability. The southern part of the Mediterranean (e.g., Egypt) reported relatively low scores for both climate and WQ, while the Northern Adriatic had the lowest WQ scores. This study represents the first attempt to evaluate Blue-NBS suitability for seagrass meadows at the eco-regional scale with geomorphologic, WQ, and climatic variables, providing decision support for the selection and allocation of Blue-NBS in different environmental settings. The resulting environmental suitability maps represent a basis for the integration of socio-economic and governance-related indicators into a more complex, multi-tier approach to support NBS mainstreaming.

Human-augmented large language model-driven selection of glutathione peroxidase 4 as a candidate blood transcriptional biomarker for circulating erythroid cells

The identification of optimal candidate genes from large-scale blood transcriptomic data is crucial for developing targeted assays to monitor immune responses. Here, we introduce a novel, optimized large language model (LLM)-based approach for prioritizing candidate biomarkers from blood transcriptional modules. Focusing on module M14.51 from the BloodGen3 repertoire, we implemented a multi-step LLM-driven workflow. Initial high-throughput screening used GPT-4, Claude 3, and Claude 3.5 Sonnet to score and rank the module’s constituent genes across six criteria. Top candidates then underwent high-resolution scoring using Consensus GPT, with concurrent manual fact-checking and, when needed, iterative refinement of the scores based on user feedback. Qualitative assessment of literature-based narratives and analysis of reference transcriptome data further refined the selection process. This novel multi-tiered approach consistently identified Glutathione Peroxidase 4 (GPX4) as the top candidate gene for module M14.51. GPX4’s role in oxidative stress regulation, its potential as a future drug target, and its expression pattern across diverse cell types supported its selection. The incorporation of reference transcriptome data further validated GPX4 as the most suitable candidate for this module. This study presents an advanced LLM-driven workflow with a novel optimized scoring strategy for candidate gene prioritization, incorporating human-in-the-loop augmentation. The approach identified GPX4 as a key gene in the erythroid cell-associated module M14.51, suggesting its potential utility for biomarker discovery and targeted assay development. By combining AI-driven literature analysis with iterative human expert validation, this method leverages the strengths of both artificial and human intelligence, potentially contributing to the development of biologically relevant and clinically informative targeted assays. Further validation studies are needed to confirm the broader applicability of this human-augmented AI approach.

Reducing mental health stigma in academia: Assessing the acceptability of a first-person narrative program.

Academics are more likely to experience mental health difficulties like depression and anxiety. However, stigma remains a key barrier to help-seeking, and limited data exists on stigma reduction for academics. This study examines the acceptability of a first-person narrative-based antistigma program conducted by Dragonfly Mental Health at a large public university. This intervention used a multitiered approach that leverages education and contact strategies to reduce stigma among graduate students, postdoctoral fellows, staff, and faculty. Personal stories of six local faculty with lived experience of mental health struggles were shown in a short film during three separate departmental retreats, followed by moderated large and small group discussions. This study presents descriptive survey data from academics (N = 149) who attended one of these three programs. A thematic content analysis was conducted on open-ended responses from 86 participants. Overall, the Dragonfly Mental Health program was well-received: 95% of participants found the film beneficial, and over 92% felt the film and discussions reduced stigma. Qualitative analysis highlighted several themes on participants' experiences. Participants appreciated the open, honest disclosures of faculty members in the film but reported that the vague disclosures perpetuated stigma. Participants liked the normalization of mental illness but desired more representation from the mental health spectrum. Participants expressed frustration over an academic work culture that is defined by overwork and burnout. These findings suggest that sharing faculty stories is a powerful mechanism to build connectedness among academics, promote open communication, and foster change in academic culture around mental health. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A proof-of-concept multi-tiered Bayesian approach for the integration of physiochemical properties and toxicokinetic time-course data for Daphnia magna

The use of in silico and in vitro methods, commonly referred to as New Approach Methodologies (NAMs), has been proposed to support environmental (and human) chemical safety decisions, ensuring enhanced environmental protection. Toxicokinetic models developed for environmentally relevant species are fundamental to the deployment of a NAMs-based safety strategy, enabling the conversion between external and internal chemical concentrations, although they require historical toxicokinetic data and robust physical models to be considered a viable solution. Daphnia magna is a key model organism in ecotoxicology albeit with limited and scattered quantitative toxicokinetic data, as for most invertebrates, resulting in a lack of robust toxicokinetic models. Moreover, current D. magna models are chemical specific, which restricts their applicability domain. One aim of this study was to address the current data availability limitations by collecting toxicokinetic time-course data for D. magna covering a broad chemical space and assessing the dataset's uniqueness. The collated toxicokinetic dataset included 48 time-courses for 30 chemicals from 17 studies, which was developed into an R package named AquaTK, with 11 studies unique to our work when compared to existing databases. Subsequently, a proof-of-concept Bayesian analysis was developed to estimate the steady-state concentration ratio (internal concentration / external concentration) from the data at three different levels of precision given three different data availability scenarios for environmental risk assessment. Specifically, an atrazine case study illustrates the multi-level modelling approach providing improvements (uncertainty reductions) in predictions of ratios for increasing amounts of data availability. Our work provides a consistent and self-contained Bayesian framework that irrespective of the hierarchy or resolution of individual experiments, can utilise the available information to generate optimal predictions of steady-state concentration ratios in D. magna. This approach is paramount to supporting the implementation of a NAMs based environmental safety paradigm shift in environmental risk assessment.

Fairness-Aware Federated Learning with Real-Time Bias Detection and Correction

Federated Learning (FL) enables collaborative model training across decentralized devices while preserving user data privacy. However, disparities in data distributions among clients can lead to biased models that perform unfairly across different demographic groups. This paper proposes a fairness-aware Federated Learning framework equipped with real-time bias detection and correction mechanisms. Our approach adjusts model updates to address biases detected at local client levels before aggregating them at the central server. We demonstrate the effectiveness of our method through empirical evaluations on multiple datasets, showcasing significant improvements in fairness and model accuracy. Our proposed framework involves a multi-tiered approach to ensure fairness in the model training process. Firstly, it employs local bias detection techniques at the client level to identify disparities in model performance across different groups. Clients then utilize bias correction mechanisms to adjust their model updates, addressing any detected biases before sending updates to the central server. The central server aggregates these bias-corrected updates, ensuring that the global model benefits from equitable learning while maintaining overall performance.

Referring wisely: knowing when and how to make subspecialty consultations in hospital medicine.

Subspecialty consultations are becoming highly prevalent in hospital medicine, due to an ageing population with multimorbid conditions and increasingly complex care needs, as well as medicolegal fears that lead to widespread defensive medical practices. Although timely subspecialty consultations in the appropriate clinical context have been found to improve clinical outcomes, there remains a significant proportion of specialty referrals in hospital medicine which are inappropriate, excessive, or do not add value to patient care. In this article, we sought to provide an overview of the common problems pertaining to excessive quantity and suboptimal quality of inpatient subspecialty consultations made in real-world practice and highlight their implications for healthcare financing and patient care. In addition, we discuss the underlying contributing factors that predispose to inappropriate use of the specialist referral system. Finally, we offer a practical, multitiered approach to help rationalize subspecialty consultations, through (i) a systematic model ('WISE' template) for individual referral-making, (ii) development of standardized healthcare institutional referral guidelines with routine clinical audits for quality control, (iii) adopting an integrated generalist care model, and (iv) incorporating training on effective referral-making in medical education.

Modulating antibody N-glycosylation through feed additives using a multi-tiered approach.

Glycosylation of recombinant proteins is a post-translational modification that affects multiple physicochemical and biological properties of proteins. As such, it is a critical quality attribute that must be carefully controlled during protein production in the pharmaceutical industry. Glycosylation can be modulated by various conditions, including the composition of production media and feeds. In this study, the N-glycosylation-modulating effects of numerous compounds, including metal enzyme cofactors, enzyme inhibitors, and metabolic intermediates, were evaluated. Chinese hamster ovary cells producing three different IgG antibodies were cultivated in a fed-batch mode. First, a one-factor-at-a-time experiment was performed in 24-well deep well plates to identify the strongest modulators and appropriate concentration ranges. Then, a full response surface experiment was designed to gauge the effects and interactions of the 14 most effective hit compounds in an Ambr® 15 bioreactor system. A wide range of glycoform content was achieved, with an up to eight-fold increase in individual glycoforms compared to controls. The resulting model can be used to determine modulator combinations that will yield desired glycoforms in the final product.

Cell Proliferation and Cytotoxicity Assays, The Fundamentals for Drug Discovery

Review Cell Proliferation and Cytotoxicity Assays, The Fundamentals for Drug Discovery Jingyi Niu†, Minai Li† and Ying Wang* State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macao 999078, China * Correspondence: emilyywang@um.edu.mo † These authors contributed equally to this work Received: 6 May 2024; Revised: 19 June 2024; Accepted: 21 June 2024; Published: 20 August 2024 Abstract: Cell proliferation and cytotoxicity assays are fundamental to drug discovery. This review summarizes prevalent methodologies for assessing cell proliferation and cytotoxicity, including direct cell count, metabolic activity, luminescent labeling, and tri-color viability imaging. The critical determinants that can significantly impact these assay outcomes, such as cellular doubling time, transitional states like quiescence and autophagy, cell cycle stages, metabolic enzyme functions, and genetic variability, are also explored. It is necessary to integrate the commonly used assays with additional analytical techniques to achieve precision in drug discovery. A multi-tiered approach that combines cellular assays with molecular analyses can improve screening processes, reduce false negatives, and increase confidence in the therapeutic potential of lead compounds.

Child Welfare Outcomes After Attachment and Biobehavioral Catch-Up Intervention Participation: A Quasi-Experimental Study.

Limited research is available examining distal child welfare outcomes after participation in evidence-based parenting interventions. To address this gap, this study employed a multi-tiered analytic approach to examine child welfare outcomes after participation in Attachment and Biobehavioral Catch-up (ABC). Using propensity score analytic techniques to establish a matched comparison group, logistic regressions examined subsequent maltreatment reports and substantiation, and survival analyses observed time to and likelihood of reunification for children who received one of three ABC curriculums compared to comparison group children (child welfare services as usual). In total, 205 children were included in the impact analysis (n = 66 treatment; n = 139 comparison); the majority of the children were White (53.7%), non-Hispanic (84.4%), males (59.5%) with an average age of 6 months (M [SD] = .50 [1.0]). Over half (56.1%) of the study sample was in out-of-home placement; 23.5% of the removed children experienced reunification. No statistically significant group differences were observed on the likelihood of subsequent or substantiated maltreatment reports. All three ABC curriculums were associated with a statistically significant increased likelihood of reunification, when compared to their matched counterpart. Additional research is warranted, though results indicate ABC may be a promising intervention to help enhance the likelihood of reunification.

LTEM: Lightweight Trust Evaluation Model in IoT Environment

In the domain of the Internet of Things (IoT), data reliability is important, particularly within critical sectors like healthcare, environmental surveillance, and smart grids. Nevertheless, data transfer from the physical domain to the digital layer is susceptible to trust-related challenges encompassing data integrity, genuineness, and credibility. Predominantly, prevailing models for trust assessment primarily concentrate on the conduct of nodes, thus disregarding the direct evaluation of data packets. This particular constraint results in an insufficient validation of data credibility, thereby failing to consider pivotal elements like timeliness and accuracy. Furthermore, utilizing cloud-based packet evaluation frameworks frequently leads to inaccuracies, unreliability, and energy inefficiencies owing to the transfer of untrusted data. The current study introduces a streamlined trust assessment framework called the lightweight trust evaluation model (LTEM), custom-built for IoT settings to combat these obstacles. LTEM meticulously examines node behavior and data packets via a multi-tiered approach encompassing nodes, cluster heads (CH), and base stations (BS). Moreover, the proposed model's architecture considers energy usage by averting the transmission of untrusted data. Simulation results showcase the supremacy of LTEM compared to existing models by achieving a detection rate of 99% for untrusted data packets, outperforming the detection rates ranging from 30% to 75% observed in other models. Moreover, LTEM enhances the operational efficiency of sensor nodes regarding energy consumption, achieving an average energy utilization of 1.33J out of 4J, resulting in savings of approximately 2.67J on average, thereby extending the lifespan of nodes.

SAFE SPACE: A SUICIDE PREVENTION HELPLINE

Abstract – The rising incidence of suicide represents a profound public health crisis that demands immediate and effective intervention. Safe Space is an innovative suicide prevention helpline designed to offer timely, compassionate, and effective support to individuals in crisis. This project aims to establish a robust, accessible helpline service leveraging both human empathy and advanced technologies to provide comprehensive care and intervention. Safe Space operates through a structured, multi-tiered approach to manage incoming calls from individuals experiencing suicidal thoughts. The process begins with call reception, where trained counselors conduct an initial assessment to evaluate the caller’s immediate risk level. Based on this assessment, callers are categorized into low, medium, or high-risk groups, guiding the subsequent intervention steps. For low-risk callers, Safe Space provides emotional support, coping strategies, and referrals to local mental health resources. Medium-risk callers receive personalized safety plans and involvement from trusted individuals in their support network. High-risk callers are provided with immediate crisis intervention, including activating emergency services if necessary. KEYWORDS – Node.js, Tailwind CSS, JavaScript, Rental Accommodation, Mess Facilities, Web-Based Platform.