Bibliographic record
Abstract
Purpose Many low- and middle-income countries (LMICs) are struggling to reduce maternal mortality rates, despite increased efforts by the United Nations through the implementation of their Millennium Development Goals program. Industrialized nations, such as Canada, have a collaborative role to play in raising the global maternal health standards. The purpose of this paper is to propose policy approaches for Canadians and other Organization of Economic Cooperation and Development (OECD) nations who wish to assist in reducing maternal mortality rates. Design/methodology/approach Ten Canadian health experts with experience in global maternal health were interviewed. Using qualitative analytical methods, the authors coded and themed their responses and paired them with peer-reviewed literature in this area to establish a model for improving global maternal health and survival rates. Findings Findings from this study indicated that maternal health may be improved by establishing a collaborative approach between interdisciplinary teams of health professionals (e.g. midwives, family physicians, OB/GYNs and nurses), literacy teachers, agriculturalists and community development professionals (e.g. humanitarians with diverse linguistic and cultural backgrounds). From this, a conceptual approach was devised for elevating the standard of maternal health. This approach includes specifications by which maternal health may be improved, such as gender justice, women’s literacy, freedom from violence against women, food and water security and healthcare accessibility. This model is based on community health center (CHC) models that integrate upstream changes with downstream services may be utilized by Canada and other OECD nations in efforts to enhance maternal health at home and abroad. Research limitations/implications Maternal mortality may be reduced by the adoption of a CHC model, an approach well suited for all nations regardless of economic status. Establishing such a model in LMICs would ideally establish long-term relationships between countries, such as Canada and the LMICs, where teams from supporting nations would collaborate with local Ministries of Health, non-government organizations as well as traditional birth attendants and healthcare professionals to reduce maternal mortality. Practical implications All OECD Nations ought to donate 0.7 percent of their GDP toward international community development. These funds should break the tradition of “tied aid”, thereby removing profit motives, and genuinely contribute to the wellbeing of people in LMICs, particularly women, children and others who are vulnerable. The power of partnerships between people whose aims are genuinely focused on caring is truly transformative. Social implications Canada is not a driver of global maternal mortality reduction work but has a responsibility to work in partnership with countries or regions in a humble and supportive role. Applying a comprehensive and interdisciplinary approach to reducing maternal mortality in the Global South includes adopting a CHC model: a community development approach to address social determinants of health and integrating various systems of evidence-informed healthcare with a commitment to social justice. Interdisciplinary teams would include literacy professionals, researchers, midwives, nurses, family physicians, OB/GYNs and community development professionals who specialize in anti-poverty work, mediation/dialogue and education campaigns that emphasize the value of all people regardless of their gender, ethnicity, religion and income. Diasporic Canadians are invaluable members of these teams due to their linguistic and cultural knowledge as well as their enthusiasm for working with their countries of origin. Establishment of long-term partnerships of 5–10 years between a Canadian team and a region or nation in the Global South that is dedicated to reducing maternal mortality and improving women’s health are valuable. Canada’s midwifery education programs are rated as world leaders so connecting midwives from Canada with those of the Global South will facilitate essential transfer of knowledge such as using birth plans and other evidence-based practices. Skilled attendants at the birth place will save women’s lives; in most cases, trained midwives are the most appropriate attendants. Video link to a primer about this paper by Dr Farah Shroff: https://maa.med.ubc.ca/videos-and-media/ . Originality/value There are virtually no retrievable articles that document why OECD nations ought to work with nations in the LMICs to improve maternal health. This paper outlines the reasons why it is important and explains how to do it well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".