Bridging Indigenous and science-based knowledge in coastal and marine research, monitoring, and management in Canada

Background: Drawing upon multiple types of knowledge (e.g., Indigenous knowledge, local knowledge, science-based knowledge) strengthens the evidence-base for policy advice, decision making, and environmental management. While the benefits of incorporating multiple types of knowledge in environmental research and management are many, doing so has remained a challenge. This systematic map examined the extent, range, and nature of the published literature (i.e., commercially published and grey) that seeks to respectively bridge Indigenous and science-based knowledge in coastal and marine research and management in Canada. Methods: This systematic map applied standardized search terms across four databases focused on commercially published literature, carefully selected specialist websites, and two web-based search engines. In addition, reference sections of relevant review articles were cross-checked to identify articles that may not have been found using the search strategy. Search results were screened in two sequential stages; (1) at title and abstract; and (2) at full text following a published protocol. All case studies included were coded using a standard questionnaire. A narrative synthesis approach was used to identify trends in the evidence, knowledge gaps, and knowledge clusters. Results: A total of 62 articles that spanned 71 Canadian case studies were included in the systematic map. Studies across the coastal and marine regions of Inuit Nunangat accounted for the majority of the studies. Whether the focus is on management and decision making or research and monitoring, the predominant ecological scale was at the species level, accounting for over two-thirds of the included studies. There were 24 distinct coastal and marine species of central focus across the studies. Nunavut had the greatest taxonomic coverage as studies conducted to date cover 13 different genera. The predominant methodology employed for combining and/or including Indigenous knowledge was case study design, which accounted for over half of the studies. Other methodologies employed for combining and/or including different ways of knowing included: (i) community-based participatory research; (ii) mixed methods; (iii) ethnography; and (iv) simulation modelling. There are a suite of methods utilized for documenting and translating Indigenous knowledge and an equally diverse tool box of methods used in the collection of scientific data. Over half of the case studies involved Indigenous knowledge systems of the Inuit, while another significant proportion involved Indigenous knowledge systems of First Nations, reflecting 21 unique nations. We found that demographics of knowledge holders were generally not reported in the articles reviewed. Conclusions: The results of this systematic m

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran