MétaCan
Menu
Back to cohort
Record W2064340879 · doi:10.1080/17441692.2012.762687

Health system reform and safe abortion: A case study of Mongolia

2013· article· en· W2064340879 on OpenAlexaff
Christina S. Beck, Nicole S. Berry, Semjidmaa Choijil

Bibliographic record

VenueGlobal Public Health · 2013
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAbortionReproductive healthUnsafe abortionEconomic growthAbortion lawContext (archaeology)Health policyFamily planningPrivate sectorBusinessMedicineDevelopment economicsHealth careEnvironmental healthPopulationEconomicsGeography

Abstract

fetched live from OpenAlex

Unsafe abortion serves as a marker of global inequity as it is concentrated in the developing world where the poorest and most vulnerable women live. While liberalisation of abortion law is essential to the reduction of unsafe abortion, a number of challenges exist beyond this important step. This paper investigates how popular health system reforms consonant with neoliberal agendas can challenge access to safe abortion. We use Mongolia, a country that has liberalised abortion law, yet, limited access to safe abortion, as a case study. Mongolia embraced market reforms in 1990 and subsequently reformed its health system. We document how common reforms in the areas of finance and regulation can compromise the safety of abortions as they foster challenges that include inconsistencies in service delivery that further foment health inequities, adoption of reproductive health programmes that are incompatible with the local sociocultural context, unregulated growth of the private sector and poor enforcement of standards and technical guidelines for safe abortion. We then discuss how this case study suggests the conversations that reproductive health policy-makers must have with those engineering health sector reform to ensure access to safe abortion in a liberalised environment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.038
GPT teacher head0.348
Teacher spread0.310 · 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.

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

Citations9
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Public HealthSame topicReproductive Health and ContraceptionFrench-language works237,207