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Record W2600671106 · doi:10.1080/17441692.2017.1303743

Community-based misoprostol for the prevention of post-partum haemorrhage: A narrative review of the evidence base, challenges and scale-up

2017· review· en· W2600671106 on OpenAlexaff
Karen Hobday, Jennifer Hulme, Suzanne Belton, Caroline Homer, Ndola Prata

Bibliographic record

VenueGlobal Public Health · 2017
Typereview
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMisoprostolMedicineAttendanceScale (ratio)Post partumNursingEconomic growthPregnancyGeographyEconomics

Abstract

fetched live from OpenAlex

Achieving Sustainable Development Goal targets for 2030 will require persistent investment and creativity in improving access to quality health services, including skilled attendance at birth and access to emergency obstetric care. Community-based misoprostol has been extensively studied and recently endorsed by the WHO for the prevention of post-partum haemorrhage. There remains little consolidated information about experience with implementation and scale-up to date. This narrative review of the literature aimed to identify the political processes leading to WHO endorsement of misoprostol for the prevention of post-partum haemorrhage and describe ongoing challenges to the uptake and scale-up at both policy and community levels. We review the peer-reviewed and grey literature on expansion and scale-up and present the issues central to moving forward.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.396
GPT teacher head0.488
Teacher spread0.092 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2017
Admission routes1
Has abstractyes

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