The transitioning experiences of internationally-educated nurses into a Canadian health care system: A focused ethnography

<p>Abstract</p> <p>Background</p> <p>Beyond well-documented credentialing issues, internationally-educated nurses (IENs) may need considerable support in transitioning into new social and health care environments. This study was undertaken to gain an understanding of transitioning experiences of IENs upon relocation to Canada, while creating policy and practice recommendations applicable globally for improving the quality of transitioning and the retention of IENs.</p> <p>Methods</p> <p>A focused ethnography of newly-recruited IENs was conducted, using individual semi-structured interviews at both one-to-three months (Phase 1) and nine-to-twelve months post-relocation (Phase 2). A purposive sample of IENs was recruited during their orientation at a local college, to a health authority within western Canada which had recruited them for employment throughout the region. The interviews were recorded and transcribed, and data was managed using qualitative analytical software. Data analysis was informed by Roper and Shapira's framework for focused ethnography.</p> <p>Results</p> <p>Twenty three IENs consented to participate in 31 interviews. All IENs which indicated interest during their orientation sessions consented to the interviews, yet 14 did not complete the Phase 2 interview due to reorganization of health services and relocation. The ethno-culturally diverse group had an average age of 36.4 years, were primarily educated to first degree level or higher, and were largely (under) employed as "Graduate Nurses". Many IENs reported negative experiences related to their work contract and overall support upon arrival. There were striking differences in nursing practice and some experiences of perceived discrimination. The primary area of discontentment was the apparent communication breakdown at the recruitment stage with subsequent discrepancy in expected professional role and financial reimbursement.</p> <p>Conclusions</p> <p>Explicit and clear communication is needed between employers and recruitment agencies to avoid employment contract misunderstandings and to enable clear interpretation of the credentialing processes. Pre-arrival orientation of IENs including health care communications should be encouraged and supported by the recruiting institution. Moreover, employers should provide more structured and comprehensive workplace orientation to IENs with consistent preceptorship. Similar to findings of many other studies, diversity should be valued and incorporated into the professional culture by nurse managers.</p

Fleet-Level Environmental Assessments for Feasibility of Aviation Emission Reduction Goals

13-C-AJFE-PU-013This is an open access paper under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: Ogunsina, K.E., Chao, H., Kolencherry, N.J., Jain, S., Moolchandani, K.A., DeLaurentis, D., & Crossley, W.A. (2022). Fleet-Level Environmental Assessments for Feasibility of Aviation Emission Reduction Goals. ArXiv, https://doi.org/10.48550/arXiv.2210.11302The International Air Transport Association (IATA) is one of several organizations that have presented goals for future CO2 emissions from commercial aviation with the intent of alleviating the associated environmental impacts. These goals include attaining carbon-neutral growth in the year 2020 and total aviation CO2 emissions in 2050 equal to 50% of 2005 aviation CO2 emissions. This paper presents the use of a simulation-based approach to predict future CO2 emissions from commercial aviation based upon a set of scenarios developed as part of the Aircraft Technology Modeling and Assessment project within ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment. Results indicate that, in future scenarios with increasing demand for air travel, it is difficult to reduce CO2 emissions in 2050 to levels equal to or below 2005 levels, although neutral CO2 growth after 2020 may be possible. Presented at the Council of Engineering Systems Universities (CESUN) conference in 201

Outcomes following small bowel obstruction due to malignancy in the national audit of small bowel obstruction

Introduction Patients with cancer who develop small bowel obstruction are at high risk of malnutrition and morbidity following compromise of gastrointestinal tract continuity. This study aimed to characterise current management and outcomes following malignant small bowel obstruction. Methods A prospective, multicentre cohort study of patients with small bowel obstruction who presented to UK hospitals between 16th January and 13th March 2017. Patients who presented with small bowel obstruction due to primary tumours of the intestine (excluding left-sided colonic tumours) or disseminated intra-abdominal malignancy were included. Outcomes included 30-day mortality and in-hospital complications. Cox-proportional hazards models were used to generate adjusted effects estimates, which are presented as hazard ratios (HR) alongside the corresponding 95% confidence interval (95% CI). The threshold for statistical significance was set at the level of P ≤ 0.05 a-priori. Results 205 patients with malignant small bowel obstruction presented to emergency surgery services during the study period. Of these patients, 50 had obstruction due to right sided colon cancer, 143 due to disseminated intraabdominal malignancy, 10 had primary tumours of the small bowel and 2 patients had gastrointestinal stromal tumours. In total 100 out of 205 patients underwent a surgical intervention for obstruction. 30-day in-hospital mortality rate was 11.3% for those with primary tumours and 19.6% for those with disseminated malignancy. Severe risk of malnutrition was an independent predictor for poor mortality in this cohort (adjusted HR 16.18, 95% CI 1.86 to 140.84, p = 0.012). Patients with right-sided colon cancer had high rates of morbidity. Conclusions Mortality rates were high in patients with disseminated malignancy and in those with right sided colon cancer. Further research should identify optimal management strategy to reduce morbidity for these patient groups

Livelihood Diversification Sources of Female Household Heads in Rural Communities of Osun State

The study investigated the livelihood of female household heads (FHH) in rural communities of Osun State. Specifically, it described the personal and socio-economic characteristics of female household heads, identified their livelihood sources and investigated the problems faced their sources of external assistance/support. Multi-stage sampling procedure was employed to select 120 respondents to whom structured questionnaires were administered to elicit requisite information. Frequency counts, percentage, means and standard deviation were used for data analysis. The results showed that the female household heads were mainly widows (55%) and the mean age and household size were 50.46±13.07 and 5.46±2.09, respectively. The major livelihood sources were petty trading (97.5%), crop processing (57.5%), farming (48.3%) and reselling of farm produce (22.2%). Financial difficulty was a paramount problem identified, followed by lack of contact with extension agents (71.7%) and gender discrimination in obtaining land on lease for farming (39.2). Majority (65.8%) of FHH did not have external sources of financial assistance while 21.7% were supported by their children. The study concluded that livelihoods of FHH were diversified mainly within agriculture and trading enterprises.Key words: Livelihood diversification, Female household heads, Rural communities

Application of convolutional neural networks to building segmentation in aerial images

Thesis (MSc)--Stellenbosch University, 2018.ENGLISH ABSTRACT : Aerial image labelling has found relevance in diverse areas including urban management, agriculture, climate, mining, and cartography. As a result, research efforts have been intensified to find fast and accurate algorithms. The current state-of-the-art results in this context have been achieved by deep convolutional neural networks (CNNs). This has been possible because of advances in computing technologies such as fast GPUs and the discovery of optimal architectures. One of the main challenges in using deep CNNs is the need for a large set of ground truth labels during the training phase. Moreover, one has to choose optimal values for the many hyperparameters involved in the model construction to get a good result. In this thesis we focus on building segmentation from aerial images, and study the effect of different hyperparameter values, paying particular attention to the generalisation ability of the resulting models. For all our experiments we use the same architecture and performance metric as the one used in Mnih & Hinton (2012). Our investigation found the following main results: 1) when it comes to the size of CNN filters, small size filters perform as good or even better than large sized filters; 2) the LeakyReLU activation functions lead to a better precision-recall curve than ReLU (Rectified Linear unit) and Tanh activation functions; 3) batch-normalization leads to a slightly poor breakeven point than without batch-normalization - this is contrary to what has been found in other studies with different architectures. In addition, we also investigate how well our models generalise to the task of interpreting contexts that are different from the training sets. Drawing from our findings, we gave recommendations on how to make deep CNN models more robust to variations in aerial images of other continent such as Africa where annotations are either unavailable or in short supply.AFRIKAANSE OPSOMMING : Lugfoto-etikettering het relevansie gevind in verskeie gebiede, insluitende stedelike bestuur, landbou,klimaat, mynbou en kartografie. As gevolg hiervan is navorsingspogings versterk om vinnige en akkurate algoritmes te vind. Die huidige state-of-the-art resultate in hierdie konteks is bereik deur diep konvolusie neurale netwerke (CNNs). Dit is moontlik as gevolg van vooruitgang in rekenaar tegnologie soos vinnige GPU’s en die ontdekking van optimale argitektuur. Een van die grootste uitdagings in die gebruik van diep CNN’s is die behoefte aan ’n groot aantal grondwaarheidetikette gedurende die opleidingsfase. Daarbenewens moet mens optimale waardes kies vir die baie hiperparameters wat by die modelkonstruksie betrokke is om ’m goeie resultaat te kry. In hierdie proefskrif het ons fokus op die bou van segmentering van lugfoto’s en bestudeer die effek van verskillende hiperparameterwaardes, met spesiale aandag aan die veralgemeningsvermoe van die gevolglike modelle. Vir al ons eksperimente gebruik ons dieselfde argitektuur en prestasiemetriek as die een wat in Mnih en Hinton (2012) gebruik word. Ons ondersoek het die volgende hoofresultate gevind: 1) As dit by die grootte van CNN-filters kom, doen klein grootte filters so goed of selfs beter as groot grootte filters; 2) die LeakyReLU aktiverings funksies lei tot ’n beter presisie-herhalingskromme as ReLU (reggestelde lineere eenheid) en Tanh aktiverings funksies; 3) batch-normalsering lei tot ’n effens swak gelykbreekpunt as sonder batch-normalisering dit is strydig met wat in ander studies met verskillende argitekture gevind is. Daarbenewens ondersoek ons ook hoe goed ons modelle veralgemeen in die interpretasie van kontekste wat verskil van die opleidingsstelle. Op grond van ons bevindinge, het ons aanbevelings gegee oor hoe om diep CNN-modelle sterker te maak vir variasies in lugfoto’s van ander vastelande soos Afrika waar annotasies of onbeskikbaar of in gebreke is

CCDC 279024: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures

CCDC 285445: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures

CCDC 285446: Experimental Crystal Structure Determination

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures