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Record W4392785736 · doi:10.1002/ijgo.15337

Perils and possibilities of health exception laws: A narrative review

2024· review· en· W4392785736 on OpenAlexaff
Dorothy Shaw, John Koku Awoonor‐Williams, Annika Brauer, Laura Gil, Juana Pérez Morales, Wendy Chavkin

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2024
Typereview
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarmAbortionNarrativeLawPolitical scienceMedicineCriminologySociologyPsychologyPregnancy

Abstract

fetched live from OpenAlex

Forty-seven of the 203 countries with abortion laws detailed by the Center for Reproductive Rights have a health exception (HE) clause, inconsistent in both wording and implementation, even within countries. This narrative review sought to determine the understanding and implementation of the legally permissible HE in different countries, or states, to provide clarification and guidance for strategies that will maximize permitted access to safe abortion within the law and avoid undue delays that harm the lives and health of women and their families. A multimethod approach was used. The literature search for countries with HE laws, including physical, mental, and social health, and exceptions for threat to life, rape, incest, and fetal anomaly, returned sparse results. The review of emblematic cases that had reached regional courts on the grounds of human rights violation for failure to obtain legal abortion under the country's HE clause included some examples qualifying on multiple grounds. We interviewed 15 physician advocates from 14 countries about use of the HE in their countries. Informants from Latin America interpreted the HE to refer to physical, psychological, and social health. HE laws are common but confusing, with significant opportunities to improve access through clarification and implementation. Where multiple grounds permit ending a pregnancy, the least onerous exception for the patient is the most ethical. Examples of progress in Colombia and Ghana demonstrate successful approaches to broader HE implementation.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.440
Teacher spread0.374 · 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 designOther design
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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