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Record W61634836

Mercy in action. Philippine birth center statistics.

2004· article· en· W61634836 on OpenAlexaboutno aff
Vicki Penwell

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineObstetricsFetal distressChildbirthPregnancyHome birthPovertyInfant mortalityFamily medicinePopulationNursingDemographyFetusEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

I studied 7,565 women admitted for labor and delivery in two free-standing charity birth centers that I established in the Philippines. The births occurred between February 8, 1996, and December 31, 2003. Midwives conducted all of the deliveries that occurred in the birth centers. The midwives were certified professional midwives (CPM) or licensed midwives (LM) from the USA, Canada and the Philippines. They supervised student midwives enrolled in the Mercy In Action College of Midwifery & Primary Health Care and dual-enrolled in the National College of Midwifery's Associate of Science in Midwifery program. These students were from all around the world. The birthing women were at higher than average risk of a poor pregnancy outcome because of demographic factors: most were poor, often malnourished and living in crowded urban slum conditions. Ninety-two percent of the women and 34% of their spouses were unemployed, and only a little over half were married. In spite of the poverty, 95% of the women had spontaneous vaginal birth; 83% had blood loss less than 500 ml; 85% of the babies required no resuscitation effort; 67% of the labors were without fetal distress or meconium staining; and 90% of the babies were of normal birth weight. Transfers to a hospital after admission occurred 7% of the time, with half taking place before delivery and half after delivery. Neonatal mortality was 4.1 per 1000.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.131
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.017
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0530.018

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2004
Admission routes1
Has abstractyes

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