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9 results for “Human resource management”
Financial, supplies, and human resource preparedness in management of COVID-19 pandemic among dental facilities in Nairobi County, Kenya
<p>The COVID-19 pandemic put a strain on healthcare facilities across the globe. Dental facilities pose the highest COVID-19 transmission risk categories due to the aerosol-generating procedures involved in dental practice. This study aimed to determine financial, supplies, and human resource preparedness in managing the COVID-19 pandemic among Nairobi County, Kenya dental facilities. A cross-sectional study was conducted using a mixed-methods approach among 183 dental facilities in Nairobi County. Data was collected using a KoboCollect questionnaire and analyzed using MS Excel and SPSS version 26. Dental facilities' readiness was assessed using the ReadyScore Criteria. Qualitative data was collected through one-on-one interviews with key informants and analyzed thematically. Readyscore Criteria analysis showed that 39 (21.3%) of the evaluated dental facilities were considered "ready," while 133 (72.7%) and 11 (6%) were considered to have "work to do" and "not ready" for the pandemic. Bivariate analysis showed that the level of facility (p<0.001), presence of other departments (p<0.001), funds sufficiency for COVID-19 emergency response (p=0.001), and clients attended per month (p=0.017) were statistically significant factors associated with pandemic preparedness scores. Regression analysis revealed that the presence of other departments among the dental facilities was a significant predictor of readiness, with a 4.5 times higher likelihood of being ready for a pandemic (aOR 4.591; 1.471–14.327, p=0.009) compared to other facilities. Support from healthcare authorities and capacity-building initiatives are recommended to enhance preparedness and resilience among dental facilities in the face of the COVID-19 pandemic.</p>
BIG DATA ANALYTICS IN DIGITAL HUMAN RESOURCES MANAGEMENT: IMPACT ON THE RECRUITMENT PROCESS
<p>This study investigates how HR employees experience the big data phenomenon in the recruitment function of HRM and how their perceptions of the phenomenon have evolved. This study also examines how BD will affect organizational and HRM and how it can be improved in other functions of HR. In this exploratory study, which comprehensively addresses the BD phenomenon in HRM, the phenomenological design approach, one of the qualitative research methods, was applied to test the research questions and a semi-structured interview form was used for research data. Using the snowball sampling method, in-depth interviews were conducted with 10 HR employees working in large and semi-structured organizations in Turkey and the interviews were analyzed with MAXQDA 20. The findings show that HRM employees are aware of BD. On the other hand, it is understood that BD technologies provide easy accessibility in recruitment, offer a strategic competitive advantage, and enable more effective management of information management, which saves HR employees' work in a facilitating way. They benefit from technology as a decision support assistant. Finally, the research results provide theoretical and practical implications for future researchers and practitioners for the development and effective use of BD technology in the field of HRM.</p>
Financial, supplies, and human resource preparedness in management of COVID-19 pandemic among dental facilities in Nairobi County, Kenya
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Data from: Human judgment vs. quantitative models for the management of ecological resources
Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1–3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time.
Appendix 5: Supplementary data for Chapter 4 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development'
<p>Appendix 5: Supplementary data for Chapter 4 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
Appendix 4: Supplementary data for Chapter 3 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for the management of biodiversity alongside human development'
<p>Appendix 4: Supplementary data for Chapter 3 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
Appendix 6: Supplementary data for Chapter 5 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development'
<p>Appendix 6: Supplementary data for Chapter 5 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
Data from: Human judgment vs. quantitative models for the management of ecological resources
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THE IMPORTANCE OF HUMAN RESOURCE MANAGEMENT IN AN AREA OF GLOBAL ECONOMIC DEVELOPMENT
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.