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Record W2109239468 · doi:10.1353/cpr.2015.0065

The Chilcapamba–McGill Partnership: Exploring Access to Maternal and Newborn Care in Indigenous Communities of Ecuador

2015· article· en· W2109239468 on OpenAlexaboutno aff
Annie Dubé, Gillian Bartlett, Juana Morales, Andrea Evans, Alison Doucet, Alexander Caudarella, Mélissa Roy, Doaa Farid, Ann C. Macaulay

Bibliographic record

VenueProgress in community health partnerships · 2015
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipIndigenousParticipatory action researchCommunity-based participatory researchCitizen journalismHealth careEconomic growthPolitical scienceMedicineNursingFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Based on a participatory research (PR) partnership between Family Medicine at McGill University, Canada and the Andean community of Chilcapamba, Ecuador, a medical student study focused on maternal and newborn health. OBJECTIVES: To evaluate the access to maternal and newborn care and the occurrence of intrafamilial violence in women with children 5 years of age or less in three indigenous communities of Ecuador. METHODS: A semistructured survey explored the perinatal and intrapartum care as well as intrafamilial violence. RESULTS: All women (N = 30) received prenatal care, 29 received postnatal care from a physician and 77% gave birth at the hospital. Eighty percent of women experienced intrafamilial violence; 73% reported psychological and 53% physical violence. CONCLUSIONS: There is good access to maternal and newborn health care, although the reported level of violence is high. Results were shared with the community and will be used in a local community health worker (CHW) training program. Our project highlights the importance of PR to investigate sensitive health challenges.

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 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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.004
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 head0.414
GPT teacher head0.488
Teacher spread0.075 · 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 teacher head, not a consensus.

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

Citations4
Published2015
Admission routes1
Has abstractyes

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