Pregnancy and childbirth during incarceration: A qualitative systematic review of lived experiences
Bibliographic record
Abstract
BACKGROUND: Incarcerated individuals who experience pregnancy or childbirth in correctional facilities face unique considerations for obstetric care and consequently are at greater risk of adverse maternal and fetal outcomes. OBJECTIVES: To characterise patient experiences regarding pregnancy and childbirth during incarceration via qualitative synthesis. SEARCH STRATEGY: Medline-OVID, EMBASE, CINAHL, Sociological Abstracts, Social Work Abstracts, Web of Science, Scopus and PsycInfo were systematically searched from inception to 24 December 2020. Supplementary searches were performed using the Scopus database. SELECTION CRITERIA: Only original, peer-reviewed literature was examined. Eligible studies were assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. RESULTS: After screening 4173 original database citations, 24 studies that met inclusion criteria were included and analysed via thematic analysis. The 24 studies included perspectives from 645 female patients who had experienced incarceration, 69 healthcare providers and 70 prison staff. Key patient-reported concerns for the well-being of pregnant individuals during incarceration included mental health challenges, dehumanisation of prenatal care and delivery, lack of privacy, stigma, psychological trauma, lack of emotional support and shackle usage during pregnancy and/or labour. The studies reported a lack of support for patients to access female correctional officers or guards, privacy during intimate examinations, timely medical care and support for breastfeeding. Above all, the psychological trauma of separation from one's newborn after birth was of utmost devastation. CONCLUSIONS: Our systematic review highlights the dire need for accountability and interventions to improve pregnancy and childbirth care for incarcerated individuals. TWEETABLE ABSTRACT: This systematic review describes lived experiences of pregnancy & childbirth during incarceration, including dehumanisation, psychological trauma, and use of shackles.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".