Factors influencing maternal health in indigenous communities with presence of traditional midwifery in the Americas: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Indigenous mothers often receive culturally unsafe services that do not fully respond to their needs. The objective of this scoping review is to collate and assess evidence that identifies factors, including the role and influence of traditional midwives, that affect maternal health in indigenous communities in the Americas. The results will map Western perspectives reflected in published and unpublished literature to indicate the complex network of factors that influence maternal outcomes. These maps will allow for comparison with local stakeholder knowledge and discussion to identify what needs to change to promote culturally safe care. METHODS AND ANALYSIS: A librarian will search studies with iterative and documented adjustments in CINAHL, Scopus, Latin American and Caribbean Health Sciences Literature (LILACS), MEDLINE, Embase and Google Scholar without any time restrictions, and use Google search engine for grey literature. Included studies will be empirical (quantitative, qualitative or mixed); address maternal health issues among indigenous communities in the Americas; and report on the role or influence of traditional midwives. Two researchers will independently screen and blindly select the included studies. The quality assessment of included manuscripts will rely on the Mixed Methods Appraisal Tool (MMAT). Two independent researchers will extract data on factors promoting or reducing maternal health in indigenous communities, including the role or influence of traditional midwives. Fuzzy cognitive mapping will summarise the findings as a list of relationships between identified factors and outcomes with weights indicating strength of the relationship and the evidence supporting this. ETHICS AND DISSEMINATION: This review is part of a proposal approved by the ethics committees at McGill University and the Centro de Investigación de Enfermedades Tropicales in Guerrero. Participating indigenous communities in Guerrero State approved the study in 2015. The results of the scoping review will contribute to the field of cultural safety and intercultural dialogue for the promotion of maternal health in indigenous communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.115 | 0.103 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.015 |
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 source (direct Gemma or distilled Codex), 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".