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Understanding the social organisation of maternity care systems: midwifery as a touchstone

2005· article· en· W2102068128 on OpenAlexaffabout
Cecilia Benoit, Sirpa Wrede, Ivy Lynn Bourgeault, Jane Sandall, Raymond De Vries, Edwin van Teijlingen

Bibliographic record

VenueSociology of Health & Illness · 2005
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsMcMaster UniversityUniversity of Victoria
FundersFogarty International Center
KeywordsDivision of labourMaternity careNegotiationHealth careDiversity (politics)WelfarePoliticsNursingSociologyWelfare statePolitical scienceObstetricsMedicineLawSocial science

Abstract

fetched live from OpenAlex

Theories of professions and healthcare organisation have difficulty in explaining variation in the organisation of maternity services across developed welfare states. Four countries - the United Kingdom, Finland, the Netherlands and Canada - serve as our case examples. While sharing several features, including political and economic systems, publicly-funded universal healthcare and favourable health outcomes, these countries nevertheless have distinct maternity care systems. We use the profession of midwifery, found in all four countries, as a 'touchstone' for exploring the sources of this diversity. Our analysis focuses on three key dimensions: (1) welfare state approaches to legalising midwifery and negotiating the role of the midwife in the division of labour; (2) professional boundaries in the maternity care domain; and (3) consumer mobilisation in support of midwifery and around maternity issues.

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)0.006
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0090.051
Scholarly communication0.0120.014
Open science0.0010.009
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.109
GPT teacher head0.399
Teacher spread0.290 · 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

Citations104
Published2005
Admission routes2
Has abstractyes

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