Preventing homelessness in pregnant youth and young mothers: a review of key turning points
Bibliographic record
Abstract
Background Pregnancy and motherhood in youth experiencing homelessness is a critical public health issue. Understanding key turning points (KTPs) and pathways into, during, and out of homelessness for this group is vital for enhancing prevention and intervention efforts.We conducted a scoping review to synthesize existing knowledge on the topic in scientific and grey literature for pregnant youth and young mothers aged 13 to 30.Methods The review involved an extensive search in eight scientific and three grey literature databases, and a Google Scholar search, leading to the thematic analysis of 45 studies.Results Nine themes were identified: 1. History of experiencing violence, abuse, and victimization; 2. Involvement with the child welfare system; 3. Transition periods; 4. Lack of supportive relationships; 5. Pro-pregnancy attitudes; 6. Pregnancy and motherhood as opportunity and challenge; 7. Fear of child protective services and custody loss; 8. Challenges in accessing resources; 9. Pregnancy and motherhood as a facilitator for exiting homelessness. The review also highlighted unique aspects of homelessness and pregnancy for LGBTQIA + and BIPOC youth.Conclusions The scoping review revealed complex, interconnected pathways and KTPs for pregnant youth and young mothers experiencing homelessness. Effective support requires tailored interventions addressing their specific needs, with a focus on trauma-informed care.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".