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Record W3209479112 · doi:10.1071/sh21116

Reproductive coercion and abuse in Australia: what do we need to know?

2021· article· en· W3209479112 on OpenAlexaff
Allison Carter, Deborah Bateson, Cathy Vaughan

Bibliographic record

VenueSexual Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsUnintended pregnancyReproductive healthSexual coercionCoercion (linguistics)Reproductive medicineMedicineAbortionDomestic violenceSexual abusePsychiatryCriminologyPsychologyPoison controlPregnancyMedical emergencySuicide preventionEnvironmental healthPopulationFamily planning

Abstract

fetched live from OpenAlex

Reproductive coercion and abuse refers to patterns of controlling and manipulative behaviours used to interfere with a person's reproductive health and decision-making. Unintended pregnancy, forced abortion or continuation of a pregnancy, and sexually transmissible infections all may result from reproductive coercion, which is closely associated with intimate partner and sexual violence. Clinicians providing sexual and reproductive healthcare are in a key position to identify and support those affected. Yet, reproductive coercion and abuse is not currently screened for in most settings and addressing disclosures poses many challenges. This article discusses what reproductive coercion and abuse is, who it affects, how it impacts, and potential strategies to improve identification and response.

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)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.353
Threshold uncertainty score0.701

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.004
Scholarly communication0.0040.009
Open science0.0010.004
Research integrity0.0040.008
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 head0.073
GPT teacher head0.416
Teacher spread0.343 · 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 designNot applicable
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

Citations12
Published2021
Admission routes1
Has abstractyes

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