Postpartum haemorrhage in rural Indigenous women: scoping review of a global obstetrical challenge
Bibliographic record
Abstract
We conducted a scoping review to determine incidence and risk factors for postpartum haemorrhage (PPH) in rural Indigenous women. We systematically searched PubMed (Medline), EMBASE, and CINAHL for all peer-reviewed articles and grey literature regarding Indigenous ethnicity, rural settings, and PPH incidence, risk factors, or maternal outcomes published from inception to 11 January 2021. Eleven articles were deemed relevant after screening and quality assessment using the National Institutes of Health scoring system for mixed study reviews. Of these, 3 articles were good quality, 1 was fair, and 7 were poor. Nine possible risk factors were recorded. The outcomes studied were transfusion, hysterectomy and mortality. PPH research in rural Indigenous women is scarce, mostly low quality and fails to represent most Indigenous cultures and countries. Women from Indigenous groups in rural Canada, Australia and the USA are at higher risk for PPH but specific risk factors are unknown. While widely differing populations made the data difficult to synthesise, this inaugural scoping review highlights a need for further research and increased obstetrical resources in areas where rural Indigenous women reside.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".