Childhood Maltreatment and Perinatal Complications: A Scoping Review of Official Health Data
Bibliographic record
Abstract
Child maltreatment (CM) poses significant risks to victims, resulting in enduring physical, psychological, and developmental consequences. Adult survivors of CM seem especially vulnerable to perinatal complications. However, existing research on perinatal outcomes presents mixed results and relies heavily on self-reported data, which may not align with official medical data. Hence, a systematic review using official health data may provide clarity on this association; it may orient future research and the provision of perinatal services. This scoping review aimed to synthesize and evaluate the quality of the literature that utilizes official health data to explore associations between CM and perinatal complications. Following Arksey and O'Malley's model, searches across four databases (PsycINFO, MEDLINE, Scopus, and ProQuest Dissertations/Thesis) produced 8,870 articles. After screening, 23 articles met the inclusion criteria (e.g., recorded perinatal complications using official health data, and peer-reviewed studies or dissertation). Evidence indicates CM survivors have less prenatal care visits, more fetal loss and preterm births, lower gestational age, and increases in emergency cesarean sections. Adults had more cervical insufficiency, lower episiotomies and sphincter ruptures, and overall pregnancy and postpartum complications while adolescents had lower Apgar scores. No associations were observed on other outcomes (e.g., vaginal bleeding, group B streptococcus, and fetal distress). Mixed findings emerged for other perinatal and maternal health concerns such as birth weight and blood pressure. CM survivors may face an increased risk of experiencing perinatal complications. Findings point to the relevance of leveraging health data for CM research and adopting trauma-informed practices in perinatal services.
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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.043 | 0.040 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".