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Record W2075077878 · doi:10.1080/13698575.2013.868408

The risk of being ‘too honest’: drug use, stigma and pregnancy

2013· article· en· W2075077878 on OpenAlexaboutno aff
Camille Stengel

Bibliographic record

VenueHealth Risk & Society · 2013
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingPregnancyChildbirthQualitative researchPsychologyHealth careStigma (botany)MedicineDevelopmental psychologySocial psychologyNursingPsychiatrySociology

Abstract

fetched live from OpenAlex

In this article, I examine the ways in which risk is constructed and managed by those involved in the pregnancy and childbirth of women who use drugs, including the women themselves. I discuss how constructions of risk influence maternal care outcomes and the understanding of choice, often in the form of stigmatisation. In this article, I draw on data from a qualitative research study that I conducted in 2011 in a western Canada city in which I interviewed 13 pregnant and parenting women who had used drugs during their pregnancy. In this article, I show how the everyday risk construction of pregnancy, labour and delivery is compounded significantly by drug use and the stigmatisation associated with this perceived risk-taking behaviour. The participants in the study often internalised this understanding of risk and this manifested itself in delays in accessing maternal health and social care services. The women in the study had different understandings of risk and these were structured by the women’s own understanding of general risk factors during their pregnancy, as well as their experiences of the constructions of risk and risk management by health and social care professionals. While structural life chances can constrain women’s feelings of self-efficacy, services that promote clients’ ability to make choices can facilitate reduced stigmatisation and facilitate the development of more compassionate and autonomous approaches to risk management.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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

Citations90
Published2013
Admission routes1
Has abstractyes

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