ORCID Profile
0000-0003-0566-372X
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Publisher: MDPI AG
Date: 09-09-2022
DOI: 10.3390/HEALTHCARE10091731
Abstract: While altruism has been studied in healthcare professions such as nursing and medicine, the exploration of the characteristics of altruism, as related to paramedicine and emergency care in Australia, is limited. This scoping review explores altruism in paramedicine from the perspective of the paramedic as practitioner, learner, and educator as seen through the lens of the paramedic and the patient. Also discussed is the positive impact of altruism on the patient experience of care. A scoping review was used to assess the availability of data related to altruism in paramedicine. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews was used to guide the process. Search categories were orientated around the subject (altruism) and discipline (paramedicine). A total of 27 articles are included in this scoping review. Initial searching identified 742 articles after duplicate removal, 396 articles were screened with 346 excluded. Fifty articles were full-text reviewed and 23 excluded. The final 27 were extracted following full-text screening. None of the articles are specific to altruism in paramedicine. The data related to the practice of altruism in paramedicine are extremely limited. The preponderance of data arise from Europe and North America which, due to crewing and service differences, may impact the practice of altruism in different regions. Recent changes to the scope of paramedic practice, workload, education, and case acuity may influence behaviour regarding altruism, compassion, caring, and associated caring behaviours. The practice and education of paramedics including altruism, compassion, caring and caring behaviours in the Australasian setting warrants further research.
Publisher: Elsevier BV
Date: 11-2013
Publisher: Cold Spring Harbor Laboratory
Date: 16-04-2020
DOI: 10.1101/2020.04.16.021444
Abstract: To meet the ambitious objectives of bio ersity and climate conventions, countries and the international community require clarity on how these objectives can be operationalized spatially, and multiple targets be pursued concurrently 1 . To support governments and political conventions, spatial guidance is needed to identify which areas should be managed for conservation to generate the greatest synergies between bio ersity and nature’s contribution to people (NCP). Here we present results from a joint optimization that maximizes improvements in species conservation status, carbon retention and water provisioning and rank terrestrial conservation priorities globally. We found that, selecting the top-ranked 30% (respectively 50%) of areas would conserve 62.4% (86.8%) of the estimated total carbon stock and 67.8% (90.7%) of all clean water provisioning, in addition to improving the conservation status for 69.7% (83.8%) of all species considered. If priority was given to bio ersity only, managing 30% of optimally located land area for conservation may be sufficient to improve the conservation status of 86.3% of plant and vertebrate species on Earth. Our results provide a global baseline on where land could be managed for conservation. We discuss how such a spatial prioritisation framework can support the implementation of the bio ersity and climate conventions.
Publisher: Cold Spring Harbor Laboratory
Date: 09-04-2022
DOI: 10.1101/2022.04.08.487610
Abstract: Despite the paramount role of plant ersity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is incomplete, h ering basic research and bio ersity conservation. Here, we used machine learning (random forests, extreme gradient boosting, neural networks) and conventional statistical methods (generalised linear models, generalised additive models) to model species richness and phylogenetic richness of vascular plants worldwide based on 830 regional plant inventories including c. 300,000 species and predictors of past and present environmental conditions. Machine learning showed an outstanding performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant ersity. Finally, we combined predictions from multiple modelling techniques (ensemble predictions) to reveal global patterns and centres of plant ersity at multiple resolutions down to 7,774 km 2 . Our predictive maps provide the most accurate estimates of global plant ersity available to date at grain sizes relevant for conservation and macroecology.
Publisher: Wiley
Date: 14-11-2022
DOI: 10.1111/NPH.18533
Abstract: Despite the paramount role of plant ersity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, h ering basic research and bio ersity conservation. Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment‐related hypotheses of broad‐scale vascular plant ersity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c . 300 000 species and predictors of past and present environmental conditions. Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant ersity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant ersity. Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant ersity at multiple resolutions down to 7774 km 2 . Our predictive maps provide accurate estimates of global plant ersity available at grain sizes relevant for conservation and macroecology.
Publisher: Springer Science and Business Media LLC
Date: 23-09-2021
Publisher: Springer Science and Business Media LLC
Date: 23-08-2021
DOI: 10.1038/S41559-021-01528-7
Abstract: To meet the ambitious objectives of bio ersity and climate conventions, the international community requires clarity on how these objectives can be operationalized spatially and how multiple targets can be pursued concurrently. To support goal setting and the implementation of international strategies and action plans, spatial guidance is needed to identify which land areas have the potential to generate the greatest synergies between conserving bio ersity and nature's contributions to people. Here we present results from a joint optimization that minimizes the number of threatened species, maximizes carbon retention and water quality regulation, and ranks terrestrial conservation priorities globally. We found that selecting the top-ranked 30% and 50% of terrestrial land area would conserve respectively 60.7% and 85.3% of the estimated total carbon stock and 66% and 89.8% of all clean water, in addition to meeting conservation targets for 57.9% and 79% of all species considered. Our data and prioritization further suggest that adequately conserving all species considered (vertebrates and plants) would require giving conservation attention to ~70% of the terrestrial land surface. If priority was given to bio ersity only, managing 30% of optimally located land area for conservation may be sufficient to meet conservation targets for 81.3% of the terrestrial plant and vertebrate species considered. Our results provide a global assessment of where land could be optimally managed for conservation. We discuss how such a spatial prioritization framework can support the implementation of the bio ersity and climate conventions.
No related grants have been discovered for Jan Wieringa.