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Record W2668988669 · doi:10.25071/2564-4033.40199

Fragmentation and hierarchies in Argentina’s maternal health services as barriers to access, continuity and comprehensiveness of care

2017· article· en· W2668988669 on OpenAlexfundno aff
Sabrina S. Yañez

Bibliographic record

VenueHealth Tomorrow Interdisciplinarity and Internationality · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsnot available
FundersSimon Fraser UniversityUniversidad de Buenos AiresConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsEthnographyParticipant observationFragmentation (computing)Health careHealth servicesConceptual frameworkPublic healthSpace (punctuation)Public relationsInstitutional analysisBusinessEconomic growthPolitical scienceSociologyNursingMedicineEnvironmental healthSocial sciencePopulationComputer science

Abstract

fetched live from OpenAlex

This paper aims to uncover the ways in which institutional regulations of maternal care services offered by the public health system in Argentina generate various forms of fragmentation and hierarchical organization that create barriers to access, continuity, and comprehensiveness of care. The conceptual and methodological tools of institutional ethnography are used as a guide for analysis of interviews with women and health agents from a province of the country’s Western region, as well as participant observation at regional hospitals and local health centers. The barriers identified and analyzed are related to regulations of time(s), space(s), and hierarchies among the health professions involved in service provision related to maternal health.Keywords: maternal health; institutional ethnography; institutional time; institutional space; hierarchies; pregnancy; Argentina; public healthcare

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)0.004
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.438
Teacher spread0.405 · 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 designQualitative
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

Citations2
Published2017
Admission routes1
Has abstractyes

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