Search results for: workplace well-being

Authors: Carmen García Sánchez

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The UN's 17 Sustainable Development Goals – specifically the nº 3 and nº 11- urgently call for new architectural design solutions at different design scales to increase human contact with nature in the health and wellbeing promotion of primarily urban communities. The discipline of Interior Design offers an important alternative to large-scale nature-inclusive actions which are not always possible due to space limitations. These circumstances provide an immense opportunity to integrate biophilic design, a complex emerging and under-developed approach that pursues sustainable design strategies for increasing the human-nature connection through the experience of the built environment. Biophilic design explores the diverse ways humans are inherently inclined to affiliate with nature, attach meaning to and derive benefit from the natural world. It represents a biological understanding of architecture which categorization is still in progress. The internationally renowned Danish domestic architecture built in the 1950´s and early 1960´s - a golden age of Danish modern architecture - left a leading legacy that has greatly influenced the domestic sphere and has further led the world in terms of good design and welfare. This study examines how four existing post-war domestic buildings establish a dialogue with nature and her variations over time. The case-studies unveil both memorable and unique biophilic resources through sophisticated and original design expressions, where transformative processes connect the users to the natural setting and reflect fundamental ways in which they attach meaning to the place. In addition, fascinating analogies in terms of this nature interaction with particular traditional Japanese architecture inform the research. They embody prevailing lessons for our time today. The research methodology is based on a thorough literature review combined with a phenomenological analysis into how these case-studies contribute to the connection between humans and nature, after conducting fieldwork throughout varying seasons to document understanding in nature transformations multi-sensory perception (via sight, touch, sound, smell, time and movement) as a core research strategy. The cases´ most outstanding features have been studied attending the following key parameters: 1. Space: 1.1. Relationships (itineraries); 1.2. Measures/scale; 2. Context: Context: Landscape reading in different weather/seasonal conditions; 3. Tectonic: 3.1. Constructive joints, elements assembly; 3.2. Structural order; 4. Materiality: 4.1. Finishes, 4.2. Colors; 4.3. Tactile qualities; 5. Daylight interplay. Departing from an artistic-scientific exploration this groundbreaking study provides sustainable practical design strategies, perspectives, and inspiration to boost humans´ contact with nature through the experience of the interior built environment. Some strategies are associated with access to outdoor space or require ample space, while others can thrive in a dense urban context without direct access to the natural environment. The objective is not only to produce knowledge, but to phase in biophilic design in the built environment, expanding its theory and practice into a new dimension. Its long-term vision is to efficiently enhance the health and well-being of urban communities through daily interaction with Nature.

Keywords: sustainability, biophilic design, architectural design, interior design, nature, Danish architecture, Japanese architecture

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Authors: Anne E. Goodenough

Abstract:

One of the most important roles of higher education professionals is to ensure that graduates have excellent employment prospects. This means providing students with the skills necessary to be immediately effective in the workplace. Increasingly, universities are seeking to achieve this by moving from lecture-based and campus-delivered curricula to more varied delivery, which takes students out of their academic comfort zone and allows them to engage with, and be challenged by, real world issues. One popular approach is integration of problem-based learning (PBL) projects into curricula. However, although the potential benefits of PBL are considerable, it can be difficult to devise projects that are meaningful, such that they can be regarded as mere ‘hoop jumping’ exercises. This study examines three-way partnerships between academics, students, and external link organizations. It studied the experiences of all partners involved in different collaborative projects to identify how benefits can be maximized and challenges overcome. Focal collaborations included: (1) development of real-world modules with novel assessment whereby the organization became the ‘client’ for student consultancy work; (2) frameworks where students collected/analyzed data for link organizations in research methods modules; (3) placement-based internships and dissertations; (4) immersive fieldwork projects in novel locations; and (5) students working as partners on staff-led research with link organizations. Focus groups, questionnaires and semi-structured interviews were used to identify opportunities and barriers, while quantitative analysis of students’ grades was used to determine academic effectiveness. Common challenges identified by academics were finding suitable link organizations and devising projects that simultaneously provided education opportunities and tangible benefits. There was no ‘one size fits all’ formula for success, but careful planning and ensuring clarity of roles/responsibilities were vital. Students were very positive about collaboration projects. They identified benefits to confidence, time-keeping and communication, as well as conveying their enthusiasm when their work was of benefit to the wider community. They frequently highlighted employability opportunities that collaborative projects opened up and analysis of grades demonstrated the potential for such projects to increase attainment. Organizations generally recognized the value of project outputs, but often required considerable assistance to put the right scaffolding in place to ensure projects worked. Benefits were maximized by ensuring projects were well-designed, innovative, and challenging. Co-publication of projects in peer-reviewed journals sometimes gave additional benefits for all involved, being especially beneficial for student curriculum vitae. PBL and student projects are by no means new pedagogic approaches: the novelty here came from creating meaningful three-way partnerships between academics, students, and link organizations at all undergraduate levels. Such collaborations can allow students to make a genuine contribution to knowledge, answer real questions, solve actual problems, all while providing tangible benefits to organizations. Because projects are actually needed, students tend to engage with learning at a deep level. This enhances student experience, increases attainment, encourages development of subject-specific and transferable skills, and promotes networking opportunities. Such projects frequently rely upon students and staff working collaboratively, thereby also acting to break down the traditional teacher/learner division that is typically unhelpful in developing students as advanced learners.

Keywords: higher education, employability, link organizations, innovative teaching and learning methods, interactions between enterprise and education, student experience

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Authors: Ruth Shapira

Abstract:

Introduction: The rapid urbanization of the last century confronts planners, regulatory bodies, developers and most of all the public with seemingly unsolved conflicts regarding values, capital, and wellbeing of the built and un-built urban space. There is an out of control change of scale of the urban form and of the rhythm of the urban life which has known no significant progress in the last 2-3 decades despite the on-growing urban population. It is the objective of this paper to analyze some of these fundamental issues through the case study of a relatively small town in the center of Israel (Kiryat-Ono, 36,000 inhabitants), unfold the deep structure of qualities versus disruptors, present some cure that we have developed to bridge over and humbly suggest a practice that may bring about a sustainable new urban environment based on timeless values of the past, an approach that can be generic for similar cases. Basic Methodologies:The object, the town of Kiryat Ono, shall be experimented upon in a series of four action processes: De-composition, Re-composition, the Centering process and, finally, Controlled Structural Disintegration. Each stage will be based on facts, analysis of previous multidisciplinary interventions on various layers – and the inevitable reaction of the OBJECT, leading to the conclusion based on innovative theoretical and practical methods that we have developed and that we believe are proper for the open ended network, setting the rules for the contemporary urban society to cluster by – thus – a new urban vocabulary based on the old structure of times passed. The Study: Kiryat Ono, was founded 70 years ago as an agricultural settlement and rapidly turned into an urban entity. In spite the massive intensification, the original DNA of the old small town was still deeply embedded, mostly in the quality of the public space and in the sense of clustered communities. In the past 20 years, the recent demand for housing has been addressed to on the national level with recent master plans and urban regeneration policies mostly encouraging individual economic initiatives. Unfortunately, due to the obsolete existing planning platform the present urban renewal is characterized by pressure of developers, a dramatic change in building scale and widespread disintegration of the existing urban and social tissue.Our office was commissioned to conceptualize two master plans for the two contradictory processes of Kiryat Ono’s future: intensification and conservation. Following a comprehensive investigation into the deep structures and qualities of the existing town, we developed a new vocabulary of conservation terms thus redefying the sense of PLACE. The main challenge was to create master plans that should offer a regulatory basis to the accelerated and sporadic development providing for the public good and preserving the characteristics of the place consisting of a tool box of design guidelines that will have the ability to reorganize space along the time axis in a sustainable way. In conclusion: The system of rules that we have developed can generate endless possible patterns making sure that at each implementation fragment an event is created, and a better place is revealed. It takes time and perseverance but it seems to be the way to provide a healthy and sustainable framework for the accelerated urbanization of our chaotic present.

Keywords: sustainable urban design, intensification, emergent urban patterns, sustainable housing, compact urban neighborhoods, sustainable regeneration, restoration, complexity, uncertainty, need for change, implications of legislation on local planning

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Authors: Tianqiao Zhang, Ye Tian, Yanliang Yin, Yichao Tian, Suzhai Tian, Weige Sun, Shuhui Gong, Limei Tang, Ruoliang Tang

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Introduction: Healthcare workers, especially the nurses all over the world, are highly vulnerable to work-related musculoskeletal disorders (WMSDs), experiencing high rates of neck, shoulder, and low back injuries, due to the unfavorable working conditions. To reduce WMSDs among nursing personnel, many workplace interventions have been developed and implemented. Unfortunately, the ongoing Covid-19 (SARS-CoV-2) pandemic has posed great challenges to the ergonomic practices and interventions in healthcare facilities, particularly the hospitals, since current Covid-19 mitigation measures, such as social distancing and working remotely, has substantially minimized in-person gatherings and trainings. On the other hand, hospitals throughout the world have been short-staffed, resulting in disturbance of shift scheduling and more importantly, the increased job demand among the available caregivers, particularly the doctors and nurses. With the latest development in communication technology, remote intervention measures have been developed as an alternative, without the necessity of in-person meetings. The Omaha System (OS) is a standardized classification system for nursing practices, including a problem classification system, an intervention system, and an outcome evaluation system. This paper describes the development of an OS-based ergonomic intervention program. Methods: First, a comprehensive literature search was performed among worldwide electronic databases, including PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), between journal inception to May 2020, resulting in a total of 1,418 scientific articles. After two independent screening processes, the final knowledge pool included eleven randomized controlled trial studies to develop the draft of the intervention program with Omaha intervention subsystem as the framework. After the determination of sample size needed for statistical power and the potential loss to follow-up, a total of 94 nurses from eight clinical departments agreed to provide written, informed consent to participate in the study, which were subsequently assigned into two random groups (i.e., intervention vs. control). A subgroup of twelve nurses were randomly selected to participate in a semi-structured interview, during which their general understanding and awareness of musculoskeletal disorders and potential interventions was assessed. Then, the first draft was modified to reflect the findings from these interviews. Meanwhile, the tentative program schedule was also assessed. Next, two rounds of consultation were conducted among experts in nursing management, occupational health, psychology, and rehabilitation, to further adjust and finalize the intervention program. The control group had access to all the information and exercise modules at baseline, while an interdisciplinary research team was formed and supervised the implementation of the on-line intervention program through multiple social media groups. Outcome measures of this comparative study included biomechanical load assessed by the Quick Exposure Check and stresses due to awkward body postures. Results and Discussion: Modification to the draft included (1) supplementing traditional Chinese medicine practices, (2) adding the use of assistive patient handling equipment, and (3) revising the on-line training method. Information module should be once a week, lasting about 20 to 30 minutes, for a total of 6 weeks, while the exercise module should be 5 times a week, each lasting about 15 to 20 minutes, for a total of 6 weeks.

Keywords: ergonomic interventions, musculoskeletal disorders (MSDs), omaha system, nurses, Covid-19

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Authors: Zdravko Trivic, John Chye Fung, Ruzica Bozovic-Stamenovic

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Within the context of rapidly ageing population in Singapore and the pressing demands on both caregivers and care providers, an integrated approach to ageing-friendly and ability-sensitive enabling environment becomes an imperative. This particularly applies to nursing home environments and their immediate surroundings, as they are becoming one of the main available options of long-term care for many senior adults who are unable to age at home. Yet, despite the considerable efforts to break the still predominant clinical approach to eldercare and to introduce more home-like design and person-centric care model, nursing homes keep being stigmatised and perceived as not so desirable environments to grow old in. The challenges are further emphasised by the associated physical, sensorial, psychological and cognitive declines that are the common consequences of ageing. Such declines have an immense impact on almost all aspects of older adults’ daily functioning, including problems with mobility and spatial orientation, difficulties in communication, withdrawal from social interaction, higher level of depression and decreased sense of independence and autonomy. However, typical nursing home designs tend to neglect the full capacities of balanced and carefully integrated multisensory stimuli as active component of care and ability building. This paper outlines part of a larger multi-disciplinary study of six nursing homes in Singapore, with overarching objectives to create new models of supportive nursing home environments that go beyond the clinical care model and encourage community integration with the nursing home settings. The paper focuses on the largely neglected aspects of sensorial comfort and multi-sensorial properties of nursing homes, including both indoor and immediate outdoor spaces (boundaries). The objective was to investigate the sensory rhythms and explore their role in nursing home users’ daily routine and therapeutic capacities. Socio-sensory rhythms were captured and analysed through a combination of on-site sensory recordings of “objective” quantitative sensory data (air temperature and humidity, sound level and luminance) using multi-function environment meter, perceived experienced data, spatial mapping, first-person observations of nursing home users’ activity patterns, and interviews. This was done in addition to employment of available assessment tools, such as Wisconsin Person Directed Care assessment tool, Dementia Quality of Life [DQoL] instrument, and Resident Environment Impact Scale [REIS], as these tools address the issues of sensorial experience insufficiently and selectively. Key findings indicate varied levels of sensory comfort, as well as diversity, intensity, and customisation of multi-sensory conditions within different nursing home spaces. Sensory stimulation is typically concentrated in communal living areas of the nursing homes or in the areas that often provide controlled or limited access, including specifically designed sensory rooms and outdoor green spaces (gardens and terraces). Opportunities for sensory stimulation are particularly limited for bed-bound senior residents and within more functional areas, such as corridors. This suggests that the capacities of nursing home designs to provide more diverse and better integrated pleasant sensory conditions as integrated “therapeutic devices” to build nursing home residents’ physical and mental abilities, encourage activity and improve wellbeing are far from exhausted.

Keywords: ageing-supportive environment, enabling design, multi-sensory assessment, nursing home environment

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Authors: Fatima Saeed, Abdullah Mudassar

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Introduction: Patient safety remains a global priority in the ever-evolving healthcare landscape. At the forefront of this endeavor are the International Patient Safety Goals (IPSGs), a standardized framework designed to mitigate risks and elevate the quality of care. Doctors, positioned as primary caregivers, wield a pivotal role in upholding and adhering to IPSGs, underscoring the critical significance of their knowledge and understanding of these goals. This research embarks on a comprehensive exploration into the depth of Doctors ' comprehension of IPSGs, aiming to unearth potential gaps and provide insights for targeted educational interventions. Established by influential healthcare bodies, including the World Health Organization (WHO), IPSGs represent a universally applicable set of objectives spanning crucial domains such as medication safety, infection control, surgical site safety, and patient identification. Adherence to these goals has exhibited substantial reductions in adverse events, fostering an overall enhancement in the quality of care. This study operates on the fundamental premise that an informed Doctors workforce is indispensable for effectively implementing IPSGs. A nuanced understanding of these goals empowers Doctors to identify potential risks, advocate for necessary changes, and actively contribute to a safety-centric culture within healthcare institutions. Despite the acknowledged importance of IPSGs, there is a growing concern that nurses may need more knowledge to integrate these goals into their practice seamlessly. Methodology: A Comprehensive research methodology covering study design, setting, duration, sample size determination, sampling technique, and data analysis. It introduces the philosophical framework guiding the research and details material, methods, and the analysis framework. The descriptive quantitative cross-sectional study in teaching care hospitals utilized convenient sampling over six months. Data collection involved written informed consent and questionnaires, analyzed with SPSS version 23, presenting results graphically and descriptively. The chapter ensures a clear understanding of the study's design, execution, and analytical processes. Result: The survey results reveal a substantial distribution across hospitals, with 34.52% in MTIKTH and 65.48% in HMC MTI. There is a notable prevalence of patient safety incidents, emphasizing the significance of adherence to IPSGs. Positive trends are observed, including 77.0% affirming the "time-out" procedure, 81.6% acknowledging effective healthcare provider communication, and high recognition (82.7%) of the purpose of IPSGs to improve patient safety. While the survey reflects a good understanding of IPSGs, areas for improvement are identified, suggesting opportunities for targeted interventions. Discussion: The study underscores the need for tailored care approaches and highlights the bio-socio-cultural context of 'contagion,' suggesting areas for further research amid antimicrobial resistance. Shifting the focus to patient safety practices, the survey chapter provides a detailed overview of results, emphasizing workplace distribution, patient safety incidents, and positive reflections on IPSGs. The findings indicate a positive trend in patient safety practices with areas for improvement, emphasizing the ongoing need for reinforcing safety protocols and cultivating a safety-centric culture in healthcare. Conclusion: In summary, the survey indicates a positive trend in patient safety practices with a good understanding of IPSGs among participants. However, identifying areas for potential improvement suggests opportunities for targeted interventions to enhance patient safety further. Ongoing efforts to reinforce adherence to safety protocols, address identified gaps, and foster a safety culture will contribute to continuous improvements in patient care and outcomes.

Keywords: infection control, international patient safety, patient safety practices, proper medication

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Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 96