Exploring the landscape of Canadian midwifery research: strengths, gaps and priorities – results of a scoping review
Bibliographic record
Abstract
OBJECTIVES: The 2014 Lancet Series on Midwifery developed the Quality of Maternal and Newborn Care (QMNC) framework outlining care needed for all childbearing people and newborns. Furthermore, this was a global call to action to invest in research capacity building. While evidence-informed care is a cornerstone of midwifery practice, there has been limited exploration of how Canadian midwifery research priorities within the Canadian context align with the global framework. In response to the call from the Lancet series, this scoping review aimed to investigate the current strengths and gaps of midwifery research in Canada. Secondarily, our goal was to map existing Canadian evidence to the QMNC framework to guide future priority setting and build research capacity. DESIGN: A scoping review. DATA SOURCES: We searched nine electronic databases for articles up to 2022, inclusive: AMED (Allied and Complementary Medicine), CINAHL, EconLit, EMBASE, HealthSTAR, MEDLINE, PsycINFO, EmCare and Web of Science. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included research conducted by (a) Canadian midwives on Canadian and non-Canadian populations, (b) international midwives on Canadian midwifery populations or (c) non-midwife researchers on Canadian midwifery populations. DATA EXTRACTION AND SYNTHESIS: We analysed data using categories from the Lancet Series' QMNC framework. At least two independent reviewers conducted screening and data extraction. RESULTS: We identified 590 articles for inclusion. Most Canadian midwifery research is related to organisation of care and care providers, clinical practice categories including promoting normal physiological processes during pregnancy, research pertaining to prenatal and intrapartum periods, and policy. Research gaps included neonatal and postpartum outcomes, midwifery education, and midwifery values and philosophy. Lastly, there were gaps in the number of randomised trials and systematic reviews, which may impact guidance of clinical decision-making. CONCLUSIONS: There has been an exponential increase in midwifery-led research in Canada. Assessment against the QMNC framework has highlighted gaps related to research conduct, clinical and non-clinical research focuses. Identifying midwifery research priorities is an important next step of consolidating Canadian research evidence. Future directions may include collaboration with midwifery stakeholders to prioritise research topics related to improving care for clients, strengthening the profession and building research capacity.
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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 | 0.097 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.056 | 0.087 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".