11 research outputs found
Individual cognitive rehabilitation for adult cancer survivors: A proof-of-concept case series
No full textAfter cancer treatments, some cancer survivors experience physical and/or mental health symptoms impacting quality of life, such as cognitive decline. There is limited access to evidence-based individualised interventions for cancer survivors with cognitive concerns. This study’s focus was to assess feasibility and acceptability of an individually delivered Responding to Cognitive Concerns - Individual (ReCogI) program, adapted from manualised cognitive-behavioural group program Responding to Cognitive Concerns (ReCog), and to assess whether ReCogI influenced perceived cognitive function and health outcomes in people treated for cancer. Within a case series using random allocation to either 3- or 4-week baseline, four cancer survivors completed ReCogI and questionnaires regarding program satisfaction, cognitive function, depression, anxiety, fatigue, and sleep. Three of four clients showed statistically reliable improvement in self-reported cognitive function. Participants were satisfied with the intervention. Therefore, ReCogI showed promising preliminary evidence for assisting adult cancer survivors who experience cognitive problems. ACTRN12621001015831, 4/08/2021, retrospectively registered
Multifunctional polymer coatings for cell microarray applications
No full textBiocompatible coatings with suitable chemistries for the immobilization of biomolecules are increasingly in demand, as they can be applied in a wide range of biomedical applications. In particular, multifunctional polymer coatings displaying reactive functional groups for the immobilization of specific biological factors that can influence the cellular response while at the same time exhibiting low nonspecific protein adsorption and cell attachment properties have the potential to significantly advance the fields of biomaterials and regenerative medicine. In this study, multifunctional polymer surface chemistries were developed for a cell microarray application with the aim of screening cellular interactions with surface immobilized factors. Coatings were prepared by the deposition of an allylamine plasma polymer pinning layer followed by the deposition of random copolymers of glycidyl methacrylate (GMA) and poly(ethylene glycol) methacrylate (PEGMA). Coatings were characterized by X-ray photoelectron spectroscopy (XPS), infrared spectroscopy, ellipsometry, and contact angle measurements. A variety of proteins as well as synthetic polymers were printed onto copolymer-coated slides using a high-precision contact microarrayer. Printing conditions were optimized for a fluorescently labeled model protein in regard to the temperature, humidity, pin geometry, concentration, and pH of the printing solution. Finally, the suitability of the surface chemistry for the evaluation of cellular responses to surface immobilized factors in a microarray format was demonstrated using HeLa cells.Mahaveer D. Kurkuri, Chantelle Driever, Graham Johnson, Gail McFarland, Helmut Thissen and Nicolas H. Voelcke
Experiences With Social Participation in Group Physical Activity Programs for Older Adults
No full textAccepted author manuscript version reprinted, by permission, from Journal of Sport and Exercise Psychology, 2021, https://doi.org/10.1123/jsep.2020-0335. © Human Kinetics, Inc.Little is known about how social participation can be facilitated among older adults in group physical activity and its psychosocial benefits that contribute to successful aging. This study aimed to understand older adults’ experiences with social participation in group physical activity programs. Using interpretive description methodology, 16 observations, eight focus groups, and two interviews with participants unable to attend focus groups were conducted with adults 55 years and older attending programs across four recreation facilities. Group programs were found to influence social participation through (a) a meaningful context for connecting and (b) instructors’ expectations of social interaction. Social participation in these programs addressed psychosocial needs by (c) increasing social contact and interaction, (d) fostering social relationships and belonging, and (e) promoting regular engagement. Training for instructors should include balancing the physical aspects of program delivery with the social, while also considering older adults’ diverse needs and preferences for social interaction.Social Sciences and Humanities Research Council (SSHRC
Mapping trust in nurses with dimensions of trustworthy artificial intelligence: A scoping review
No full textOBJECTIVES
The overarching goal of this study is to map and synthesize the evidence on dimensions of trust that are perceived by patients to be important in their relationships and/or interactions with nurses in order to inform and envision novel approaches to developing trustworthy artificial intelligence (AI) applications . The aim is to leverage the longstanding public trust that nurses are perceived to hold and develop insight into what features of trustworthiness, if any, could potentially be built into or demonstrated by artificial intelligence models. Achieving the goal of the study will require completion of the study objectives, to: i) scope and synthesize current knowledge from the nursing trust literature to generate new insights into important human dimensions of trust in relation to nurses’ practice; ii) map and compare understandings of human and computational AI understandings of trustworthiness; iii) identify and prioritize knowledge and research gaps and recommend future research directions for development of trustworthy AI; and iv) synthesize results and develop research outputs in formats that will be readily accessible and contextually relevant for target audiences.
BACKGROUND
The use of artificial intelligence (AI) technologies in healthcare presents unique challenges and opportunities for transforming health and social services. AI has been described as “… the science and engineering of making intelligent machines, especially intelligent computer programs” (McCarthy, 2007). The implementation of AI is often envisioned to produce transformative outcomes, increasing efficiencies and supporting personalized and data-driven decision making (Brennan & Bakken, 2015; Ronquillo et al., 2021). Unfortunately, examples of AI algorithms that have produced harmful outcomes erode what growing public trust there may be. One example is a risk score algorithm that used health care costs as a proxy for illness severity; because less money is spent caring for Black patients compared to white patients, the algorithm treated Black patients as less sick (Obermeyer et al., 2019). While AI offers a lot of potential, building trustworthy AI is a substantial current challenge that must be overcome if AI is to achieve what it is envisioned to do.
Focus is rightly being paid to developing computational approaches to develop trustworthy AI that target dimensions of trust such as safety, robustness, non-discrimination, fairness, explainability, privacy, etc., (Liu et al., 2021). the tendency for AI trustworthiness work to consider largely technical perspectives is arguably an important barrier; the human aspect of trust, is largely not an area of focus. there is a pressing need to understand the human aspects of trust and integrate these with computational understandings of trustworthiness, if we are to build AI systems that are deemed truly trustworthy.
Integrating understandings of trustworthiness from clinical and computational perspectives require interdisciplinary perspectives and approaches. Year upon year, public opinion polls find nurses to be the most trustworthy profession (Stutzer & Rodriguez, 2020). Besides, the literature on nursing trust highlights the importance of human dimensions that facilitate trust such as nurses’ level of knowledge and being guided by evidence, and acting ethically and with empathy (Dinç & Gastmans, 2013; Rørtveit et al., 2015). The expertise of nurses in fostering trusting relationships and the breadth of literature on nursing trust is a valuable resource that has, so far, remained underexplored for the purpose of developing trustworthy AI.
METHODS
The updated Joanna Briggs Institute (JBI) methodology for scoping reviews (Peters et al., 2020) that incorporates enhancements by Levac et al. (2010) and provides methodological clarity in response to criticism to Arksey and O’Malley’s approach will be used. In adherence to this methodology and to increase the transparency in the process, the protocol for the scoping review will be developed and registered through the Open Science Framework. A draft CINAHL search strategy was developed which combined the concepts nursing and trust. The search strategy was developed and conducted by a nursing specialist librarian and information specialist. Final refinement of the search strategy was conducted through the through the Peer Review of Electronic Search Strategies guidelines (McGowan et al., 2016) where a librarian external to the team will reviewed the search strategy. Feedback from the PRESS process were incorporated into the search strategy. Databases searched include Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, MEDLINE, PsycINFO, and forward and backward citation searching. The searches were conducted in October 2022.
Results from the completed searches will be uploaded into the Covidence literature synthesis software to remove duplicates and screen articles. Screening and selection will adhere to the pre-specified inclusion and exclusion criteria outlined in the study protocol (e.g., exclusion of grey literature). A minimum of two researchers will review all titles and abstracts for relevance and inclusion. Included articles will proceed to full-article review and further evaluation of inclusion. Any disagreements will be reviewed with the PI and resolved through consensus.
Data extraction will be completed by two researchers using a piloted data extraction form including standard categories (e.g., author(s), year of publication, study aims/purpose, population and sample size (if applicable), methodology and methods used, etc.) and key findings relating to the scoping review question (Peters et al., 2020). A thematic analysis approach and iterative synthesis of findings by the team will be used to guide the collation and summarization of findings in order to identify themes - an adaptation of the approach by Crampton et al. (2016). First, a minimum of two researchers will read and annotate each article using computational dimensions of trustworthy AI as identified by Liu et al. (2021) (safety & robustness, non-discrimination & fairness, explainability, privacy, accountability & auditability, and environmental well-being). Where there is uncertainty in whether a dimension of trustworthiness identified in the articles are captured within existing categories and/or whether a dimension fits under more than one category, the PI will discuss with the student researchers and the team will make a decision on how the dimension will be categorized and whether any new dimensions need to be named. Next, the team will review all of the articles, compare and finalize their annotations of trustworthiness dimensions as they correspond with the articles. Points of disagreement will be discussed as a team until a consensus is reached.
Results: The scoping review and the journal article will be completed in 18 months (est April 2024). All-team meetings will be held at regular intervals, which will bring together the student researchers and interdisciplinary collaborators at key points during the study. . The results will describe where research has been done, what AI technologies have been developed and studied, how these technologies have been evaluated and how nurses have participated and how ethical issues have been addressed in the research.
Conclusion: Research findings will contribute to the larger vision of developing trustworthy AI in health and social systems and enrich understandings of dimensions of trust that may have computational parallels for trustworthy AI systems.
References
Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19-32.
Brennan, P. F., & Bakken, S. (2015). Nursing Needs Big Data and Big Data Needs Nursing. J Nurs Scholarsh, 47(5), 477-484. https://doi.org/10.1111/jnu.12159
Crampton, N. H., Reis, S., & Shachak, A. (2016). Computers in the clinical encounter: a scoping review and thematic analysis. Journal of the American Medical Informatics Association, 23(3), 654-665. https://doi.org/10.1093/jamia/ocv178
Dinç, L., & Gastmans, C. (2013). Trust in nurse–patient relationships: A literature review. Nursing Ethics, 20(5), 501-516. https://journals.sagepub.com/doi/10.1177/0969733012468463?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed
Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation Science, 5(1), 1-9.
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A. K., & Tang, J. (2021). Trustworthy AI A computational perspective. arXiv preprint arXiv:2107.06641. https://arxiv.org/abs/2107.06641v3
McCarthy, J. (2007). What is artificial intelligence?
McGowan, J., Sampson, M., Salzwedel, D. M., Cogo, E., Foerster, V., & Lefebvre, C. (2016). PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement. J Clin Epidemiol, 75, 40-46. https://doi.org/10.1016/j.jclinepi.2016.01.021
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Peters, M. D., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI evidence synthesis, 18(10), 2119-2126.
Ronquillo, C. E., Peltonen, L. M., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., Nyrup, R., Rahimi, S., Reed, D. N., Salakoski, T., Salanterä, S., Walton, N., Weber, P., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, Article JAN-2021-0206.
Rørtveit, K., Hansen, B. S., Leiknes, I., Joa, I., Testad, I., & Severinsson, E. (2015). Patients' experiences of trust in the patient-nurse relationship-a systematic review of qualitative studies.
Stutzer, K., & Rodriguez, A. M. (2020). Moral resilience for critical care nurses. Critical Care Nursing Clinics, 32(3), 383-393. https://www.ccnursing.theclinics.com/article/S0899-5885(20)30041-1/fulltext
Von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., Pruinelli, L., Ronquillo, C. E., Topaz, M., & Peltonen, L.-M. (2021). Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 104153. https://doi.org/https://doi.org/10.1016/j.ijnurstu.2021.10415
Mapping public trust in nurses with dimensions of trustworthy artificial intelligence: A scoping review
No full textMapping public trust in nurses with dimensions of trustworthy artificial intelligence: A scoping review
OBJECTIVES
The goal of this study is to map and synthesize the evidence on dimensions of trust that are perceived by patients to be important in their relationships and/or interactions with nurses in order to inform and envision novel approaches to developing trustworthy artificial intelligence (AI) applications. The overarching aim is to leverage the longstanding public trust that nurses are perceived to hold and develop insight into what features of trustworthiness, if any, could potentially be demonstrated by artificial intelligence models. Achieving the goal of the study will require completion of the following objectives, to: i) scope and synthesize current knowledge from the nursing trust literature to generate new insights into important human dimensions of trust in relation to nurses’ practice; ii) map and compare understandings of human and computational AI and understandings of trustworthiness; iii) identify and prioritize knowledge and research gaps and recommend future research directions for development of trustworthy AI; and iv) synthesize results and develop research outputs in formats that will be readily accessible and contextually relevant for researchers and knowledge users.
METHODS
The updated Joanna Briggs Institute (JBI) methodology for scoping reviews (Peters et al., 2020) that incorporates enhancements by Levac et al. (2010) and provides methodological clarity in response to criticism to Arksey and O’Malley’s (2005) approach will be used. In adherence to this methodology and to increase the transparency in the process, the protocol for the scoping review will be developed and registered through the Open Science Framework. A draft Cumulative Index to Nursing and Allied Health Literature (CINAHL) search strategy was developed which combined the concepts of nursing and trust. The search strategy was developed and conducted by a nursing specialist librarian and information specialist. Final refinement of the search strategy was conducted through the Peer Review of Electronic Search Strategies (PRESS) guidelines (McGowan et al., 2016) where a librarian external to the team reviewed the search strategy. Feedback from the PRESS process were incorporated into the search strategy. Databases searched include CINAHL, EMBASE, MEDLINE, PsycINFO, and forward and backward citation searching. The searches were conducted in October 2022.
Results from the completed searches will be uploaded into the Covidence literature synthesis software to remove duplicates and screen articles. Screening and selection will adhere to the pre-specified inclusion and exclusion criteria outlined in the study protocol (e.g., exclusion of grey literature). A minimum of two researchers will review all titles and abstracts for relevance and inclusion. Included articles will proceed to full-article review and further evaluation of inclusion. Any disagreements will be reviewed with the PI and resolved through consensus.
Data extraction will be completed by two researchers using a piloted data extraction form including standard categories (e.g., author(s), year of publication, study aims/purpose, population and sample size (if applicable), methodology and methods used, etc.) and key findings relating to the scoping review question (Peters et al., 2020). A thematic analysis approach and iterative synthesis of findings by the team will be used to guide the collation and summarization of findings in order to identify themes - an adaptation of the approach by Crampton et al. (2016). First, a minimum of two researchers will read and annotate each article using computational dimensions of trustworthy AI as identified by Liu et al. (2021) (i.e., safety & robustness, non-discrimination & fairness, explainability, privacy, accountability & auditability, and environmental well-being). Where there is uncertainty in whether a dimension of trustworthiness is captured within existing categories and/or whether a dimension fits under more than one category, the PI will discuss with the team and will make a decision on how the dimension will be categorized and whether any new dimensions need to be named. Next, the team will review all of the articles, compare, and finalize the annotations of trustworthiness dimensions as they correspond with the articles. Points of disagreement will be discussed as a team until a consensus is reached.
RESULTS
The scoping review and corresponding academic manuscript will be completed in 18 months (est. April 2024). Research team meetings will be held at regular intervals, including bringing together interdisciplinary collaborators at key points during the study. The results will describe where research has been done, what AI technologies have been developed and studied, how these technologies have been evaluated and how nurses have participated, and how ethical issues have been addressed in the research.
CONCLUSION
Research findings will contribute to the larger vision of developing trustworthy AI in health and social systems and enrich understandings of dimensions of trust that may have computational parallels for trustworthy AI systems.
References
Peters, M. D., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., ... & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI evidence synthesis, 18(10), 2119-2126.
Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation science, 5(1), 1-9.
Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32.
McGowan, J., Sampson, M., Salzwedel, D. M., Cogo, E., Foerster, V., & Lefebvre, C. (2016). PRESS peer review of electronic search strategies: 2015 guideline statement. Journal of clinical epidemiology, 75, 40-46.
Crampton, N. H., Reis, S., & Shachak, A. (2016). Computers in the clinical encounter: a scoping review and thematic analysis. Journal of the American Medical Informatics Association, 23(3), 654-665.
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., ... & Tang, J. (2021). Trustworthy AI: A computational perspective. arXiv preprint arXiv:2107.06641
Mapping public trust in nurses with dimensions of trustworthy artificial intelligence: A scoping review
No full textMapping public trust in nurses with dimensions of trustworthy artificial intelligence: A scoping review
OBJECTIVES
The goal of this study is to map and synthesize the evidence on dimensions of trust that are perceived by patients to be important in their relationships and/or interactions with nurses in order to inform and envision novel approaches to developing trustworthy artificial intelligence (AI) applications. The overarching aim is to leverage the longstanding public trust that nurses are perceived to hold and develop insight into what features of trustworthiness, if any, could potentially be demonstrated by artificial intelligence models. Achieving the goal of the study will require completion of the following objectives, to: i) scope and synthesize current knowledge from the nursing trust literature to generate new insights into important human dimensions of trust in relation to nurses’ practice; ii) map and compare understandings of human and computational AI and understandings of trustworthiness; iii) identify and prioritize knowledge and research gaps and recommend future research directions for development of trustworthy AI; and iv) synthesize results and develop research outputs in formats that will be readily accessible and contextually relevant for researchers and knowledge users.
METHODS
The updated Joanna Briggs Institute (JBI) methodology for scoping reviews (Peters et al., 2020) that incorporates enhancements by Levac et al. (2010) and provides methodological clarity in response to criticism to Arksey and O’Malley’s (2005) approach will be used. In adherence to this methodology and to increase the transparency in the process, the protocol for the scoping review will be developed and registered through the Open Science Framework. A draft Cumulative Index to Nursing and Allied Health Literature (CINAHL) search strategy was developed which combined the concepts of nursing and trust. The search strategy was developed and conducted by a nursing specialist librarian and information specialist. Final refinement of the search strategy was conducted through the Peer Review of Electronic Search Strategies (PRESS) guidelines (McGowan et al., 2016) where a librarian external to the team reviewed the search strategy. Feedback from the PRESS process were incorporated into the search strategy. Databases searched include CINAHL, EMBASE, MEDLINE, PsycINFO, and forward and backward citation searching. The searches were conducted in October 2022.
Results from the completed searches will be uploaded into the Covidence literature synthesis software to remove duplicates and screen articles. Screening and selection will adhere to the pre-specified inclusion and exclusion criteria outlined in the study protocol (e.g., exclusion of grey literature). A minimum of two researchers will review all titles and abstracts for relevance and inclusion. Included articles will proceed to full-article review and further evaluation of inclusion. Any disagreements will be reviewed with the PI and resolved through consensus.
Data extraction will be completed by two researchers using a piloted data extraction form including standard categories (e.g., author(s), year of publication, study aims/purpose, population and sample size (if applicable), methodology and methods used, etc.) and key findings relating to the scoping review question (Peters et al., 2020). A thematic analysis approach and iterative synthesis of findings by the team will be used to guide the collation and summarization of findings in order to identify themes - an adaptation of the approach by Crampton et al. (2016). First, a minimum of two researchers will read and annotate each article using computational dimensions of trustworthy AI as identified by Liu et al. (2021) (i.e., safety & robustness, non-discrimination & fairness, explainability, privacy, accountability & auditability, and environmental well-being). Where there is uncertainty in whether a dimension of trustworthiness is captured within existing categories and/or whether a dimension fits under more than one category, the PI will discuss with the team and will make a decision on how the dimension will be categorized and whether any new dimensions need to be named. Next, the team will review all of the articles, compare, and finalize the annotations of trustworthiness dimensions as they correspond with the articles. Points of disagreement will be discussed as a team until a consensus is reached.
RESULTS
The scoping review and corresponding academic manuscript will be completed in 18 months (est. April 2024). Research team meetings will be held at regular intervals, including bringing together interdisciplinary collaborators at key points during the study. The results will describe where research has been done, what AI technologies have been developed and studied, how these technologies have been evaluated and how nurses have participated, and how ethical issues have been addressed in the research.
CONCLUSION
Research findings will contribute to the larger vision of developing trustworthy AI in health and social systems and enrich understandings of dimensions of trust that may have computational parallels for trustworthy AI systems.
References
Peters, M. D., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., ... & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI evidence synthesis, 18(10), 2119-2126.
Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation science, 5(1), 1-9.
Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32.
McGowan, J., Sampson, M., Salzwedel, D. M., Cogo, E., Foerster, V., & Lefebvre, C. (2016). PRESS peer review of electronic search strategies: 2015 guideline statement. Journal of clinical epidemiology, 75, 40-46.
Crampton, N. H., Reis, S., & Shachak, A. (2016). Computers in the clinical encounter: a scoping review and thematic analysis. Journal of the American Medical Informatics Association, 23(3), 654-665.
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., ... & Tang, J. (2021). Trustworthy AI: A computational perspective. arXiv preprint arXiv:2107.06641
Field computers for spatially referenced social surveys
No full textIncludes bibliographical references.This study explores the use of a palm computer, linked to a hand held global positioning system receiver, by members of an informal settlement and a rural community to collect socio-economic (SE) data. The theoretical foundation is taken from such disciplines as Semiotics, Human Computer Interaction (HCI) and Survey Research Methods. This theory was used to develop a methodology, which enabled the researcher to investigate whether it is feasible to use icons to represent SE variables, whether HCI theory is useful for assessing the field observations of the volunteers using the palm computer, and whether the data collected is useful in terms of informal settlements and Communal Property Associations (CPA's). The research approach involved developing a set of icons, which were then pretested using feedback from volunteers in Mitchell’s Plain (Cape Town) before doing field-testing in Algeria and Imizamo Yethu. Next, two field studies were conducted. In Algeria and Imizamo Yethu, the volunteers were asked to identify a set of SE icons, they were taught how to use the palm computer and the CyberTracker software, and each volunteer was asked to conduct a mini-survey. The volunteer responses to the SE icons were analysed using semiotic criteria to determine how well they identified the icons. Next, the field observations were assessed with usability criteria from HCI. Finally, the two data sets were evaluated to determine its usefulness. The recommendations of this research are that if a set of SE icons is to be developed, the researcher suggests that symbols, which are well known by the community be used. That these symbols be tested in a 32x32 pixel format to determine if there is enough detail for recognition. Also, avoid detail in a picture that could cause confusion. Also, detailed investigations into the effect of culture, gender and background, as well as human perception is needed with the focus on communities. The main motivation for this is that informal settlements are a mix between rural, urban, educated and not, and also having different cultures. Next, it is further recommended that tests in homogeneous communities also be done to determine whether they do have the same mental concept. Further field studies are required to sort out various technical problems and to test a larger set of icons. Finally, other applications for this system should be investigated, e.g. land allocation distribution
Assessing the format and content of journal published and non-journal published rapid review reports:A comparative study
No full textBACKGROUND: As production of rapid reviews (RRs) increases in healthcare, knowing how to efficiently convey RR evidence to various end-users is important given they are often intended to directly inform decision-making. Little is known about how often RRs are produced in the published or unpublished domains, and what and how information is structured. OBJECTIVES: To compare and contrast report format and content features of journal-published (JP) and non-journal published (NJP) RRs. METHODS: JP RRs were identified from key databases, and NJP RRs were identified from a grey literature search of 148 RR producing organizations and were sampled proportionate to cluster size by organization and product type to match the JP RR group. We extracted and formally compared 'how' (i.e., visual arrangement) and 'what' information was presented. RESULTS: We identified 103 RRs (52 JP and 51 NJP) from 2016. A higher percentage of certain features were observed in JP RRs compared to NJP RRs (e.g., reporting authors; use of a traditional journal article structure; section headers including abstract, methods, discussion, conclusions, acknowledgments, conflict of interests, and author contributions; and use of figures (e.g., Study Flow Diagram) in the main document). For NJP RRs, a higher percentage of features were observed (e.g., use non-traditional report structures; bannering of executive summary sections and appendices; use of typographic cues; and including outcome tables). NJP RRs were more than double in length versus JP RRs. Including key messages was uncommon in both groups. CONCLUSIONS: This comparative study highlights differences between JP and NJP RRs. Both groups may benefit from better use of plain language, and more clear and concise design. Alternative innovative formats and end-user preferences for content and layout should be studied further with thought given to other considerations to ensure better packaging of RR results to facilitate uptake into policy and practice. STUDY REGISTRATION: The full protocol is available at: https://osf.io/29xvk/
Toward quantifying the increasing role oceanic heat in sea ice loss in the new Arctic
No full textAuthor Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 96 (2015): 2079–2105, doi:10.1175/BAMS-D-13-00177.1.The loss of Arctic sea ice has emerged as a leading signal of global warming. This, together with acknowledged impacts on other components of the Earth system, has led to the term “the new Arctic.” Global coupled climate models predict that ice loss will continue through the twenty-first century, with implications for governance, economics, security, and global weather. A wide range in model projections reflects the complex, highly coupled interactions between the polar atmosphere, ocean, and cryosphere, including teleconnections to lower latitudes. This paper summarizes our present understanding of how heat reaches the ice base from the original sources—inflows of Atlantic and Pacific Water, river discharge, and summer sensible heat and shortwave radiative fluxes at the ocean/ice surface—and speculates on how such processes may change in the new Arctic. The complexity of the coupled Arctic system, and the logistic and technological challenges of working in the Arctic Ocean, require a coordinated interdisciplinary and international program that will not only improve understanding of this critical component of global climate but will also provide opportunities to develop human resources with the skills required to tackle related problems in complex climate systems. We propose a research strategy with components that include 1) improved mapping of the upper- and middepth Arctic Ocean, 2) enhanced quantification of important process, 3) expanded long-term monitoring at key heat-flux locations, and 4) development of numerical capabilities that focus on parameterization of heat-flux mechanisms and their interactions.2016-06-0
Desiring the east: a comparative study of Middle English romance and modern popular sheikh romance
No full textThis thesis comparatively examines a selection of twenty-first century sheikh romances and Middle English romances from the fourteenth and fifteenth centuries that imagine an erotic relationship occurring between east and west. They do so against a background of conflict, articulated in military confrontation and binary religious and ethnic division. The thesis explores the strategies used to facilitate the cross-cultural relationship across such a gulf of difference and considers what a comparison of medieval and modern romance can reveal about attitudes towards otherness in popular romance.
In Chapter 1, I analyse the construction of the east in each genre, investigating how the homogenisation of the romance east in sheikh romance distances it from the geopolitical reality of those parts of the Middle East seen, by the west, to be "other". Chapter 2 examines the articulation of gender identity and the ways in which these romances subvert and reassert binary gender difference to uphold normative heterosexual relations. Chapter 3 considers how ethnic and religious difference is nuanced, in particular through the use of fabric, breaking down the disjunction between east and west. Chapter 4 investigates the way ethnicity, religion and gender affect hierarchies of power in the abduction motif, enabling undesirable aspects of the east to be recast.
The key finding of this thesis is that both romance genres facilitate the cross-cultural erotic relationship by rewriting apparently binary differences of religion and ethnicity to create sameness. While the east is figured differently in Middle English and modern sheikh romance, the strategies they use to facilitate the cross-cultural erotic relationship are similar. The thesis concludes that the constancy of certain attitudes towards the east in both medieval and modern romance reveals a persistence of conservative values in representations of the east in romance
