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Record W4312019681 · doi:10.1055/s-0042-1758481

The Intersection of Abortion and Criminalization: Abortion Access for People in Prisons

2022· review· en· W4312019681 on OpenAlexafffundabout
Martha Paynter, Wendy V. Norman

Bibliographic record

VenueSeminars in Reproductive Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of British ColumbiaUniversity of New Brunswick
FundersCanadian Institutes of Health Research
KeywordsAbortionCriminalizationUnsafe abortionReproductive healthUnintended pregnancyPopulationReproductive justiceMedicineHealth careCriminologyPolitical scienceFamily planningPregnancyPsychologyEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Most incarcerated women are of reproductive age, and more than a third of women will have an abortion during their reproductive years. Although women are the fastest growing population in Canadian prisons, no one has studied the effect of their incarceration on access to abortion services. Studies outside of Canada indicate rates of abortion are higher among people experiencing incarceration than in the general population, and that abortion access is often problematic. Although international standards for abortion care among incarcerated populations exist, there conversely appear to be no Canadian guidelines or procedures to facilitate unintended pregnancy prevention or management. Barriers to abortion care inequitably restrict people with unintended pregnancy from attaining education and employment opportunities, cause entrenchment in violent relationships, and prevent people from choosing to parent when they are ready and able. Understanding and facilitating equitable access to abortion care for incarcerated people is critical to address structural, gender-, and race-based reproductive health inequities, and to promote reproductive justice. There is an urgent need for research in this area to direct best practices in clinical care and support policies capable to ensure equal access to abortion care for incarcerated people.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.979
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.097
GPT teacher head0.442
Teacher spread0.345 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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