Integrating trauma- and violence-informed care in perinatal services to support adolescent mothers in low and middle-income countries: a call to action
Bibliographic record
Abstract
Abstract Adolescent pregnancy is a significant global health issue, particularly prevalent in low- and middle-income countries (LMICs). In these regions, adolescent pregnancy is often seen as deviant, irresponsible, and shameful behavior, impacting not just the young mother but her entire family and community. Consequently, adolescent mothers frequently face ostracization, stigma, and discrimination from their families and communities. Many also endure various forms of trauma and violence before and during pregnancy. These traumatic experiences disproportionately affect the mental health of adolescent mothers in LMICs, influencing their ability to access perinatal services and which can affect their physical health and well-being, as well as that of their unborn children. When systems, guidelines and healthcare providers in perinatal services are not supported to adopt trauma- and violence-informed care (TVIC) principles, they risk perpetuating or overlooking the trauma experienced by adolescent mothers. This paper emphasizes that the perinatal environment in LMICs often does not feel safe for either adolescent mothers or their healthcare providers, potentially leading to re-traumatization. Therefore, implementing TVIC can help create safer perinatal services for both adolescent mothers and their providers.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".