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Record W1932095273 · doi:10.1186/s12978-015-0044-5

Critical maternal health knowledge gaps in low- and middle-income countries for the post-2015 era

2015· article· en· W1932095273 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReproductive Health · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchPierre Elliott Trudeau FoundationBill and Melinda Gates Foundation
KeywordsMedicineChildbirthPsychological interventionMaternal deathReproductive healthHealth careHealth policyNursingGlobal healthQuality (philosophy)Public healthEnvironmental healthPopulationPregnancyEconomic growth

Abstract

fetched live from OpenAlex

Effective interventions to promote maternal health and address obstetric complications exist, however 800 women die every day during pregnancy and childbirth from largely preventable causes and more than 90% of these deaths occur in low and middle income countries (LMIC). In 2014, the Maternal Health Task Force consulted 26 global maternal health researchers to identify persistent and critical knowledge gaps to be filled to reduce maternal morbidity and mortality and improve maternal health. The vision of maternal health articulated was comprehensive and priorities for knowledge generation encompassed improving the availability, accessibility, acceptability, and quality of institutional labor and delivery services and other effective interventions, such as contraception and safe abortion services. Respondents emphasized the need for health systems research to identify models that can deliver what is known to be effective to prevent and treat the main causes of maternal death at scale in different contexts and to sustain coverage and quality over time. Researchers also emphasized the development of tools to measure quality of care and promote ongoing quality improvement at the facility, district, and national level. Knowledge generation to improve distribution and retention of healthcare workers, facilitate task shifting, develop and evaluate training models to improve "hands-on" skills and promote evidence-based practice, and increase managerial capacity at different levels of the health system were also prioritized. Interviewees noted that attitudes, behavior, and power relationships between health professionals and within institutions must be transformed to achieve coverage of high-quality maternal health services in LMIC. The increasing burden of non-communicable diseases, urbanization, and the persistence of social and economic inequality were identified as emerging challenges that require knowledge generation to improve health system responses and evaluate progress. Respondents emphasized evaluating effectiveness, feasibility, and equity impacts of health system interventions. A prominent role for implementation science, evidence for policy advocacy, and interdisciplinary collaboration were identified as critical areas for knowledge generation to improve maternal health in the post-2015 era.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.374
Teacher spread0.339 · 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