Evidence‐based priorities of under‐served pregnant and parenting adolescents: addressing inequities through a participatory approach to contextualizing evidence syntheses
Bibliographic record
Abstract
PURPOSE: This study describes an interdiscursive evidence-based priority setting process with pregnant and parenting adolescents and their services providers. METHODS: A mixed methods literature review identified studies reporting on perinatal outcomes and experiences of adolescents during pregnancy to 12 months post-partum published in Canada after 2000. We also calculated relative risks for common perinatal risk factors and outcomes for adolescents compared to adult populations from 2012 to 2017 based on data from a provincial database of maternal and newborn outcomes. Two trained peer researchers identified outcomes most relevant to their peers. We shared syntheses results with four service providers and 13 adolescent mothers accessing services at a community service organization, who identified and prioritized their areas of concern. We repeated the process for the identified priority issue and expanded upon it through semi-structured interviews. RESULTS: Adolescent mothers face higher rates of poverty, abuse, anxiety and depression than do adult mothers. Adolescents prioritized the experience of judgment in perinatal health and social services, particularly as it contributed to them being identified as a child protection risk. Secondary priorities included loss of social support and inaccessibility of community resources. The experience of judgment in adolescent perinatal health literature was summarized around: being invisible, seen as incapable and seen as a risk. Adolescent mothers adapted these categories, emphasizing organizational and social barriers. CONCLUSIONS: Young marginalized women are disproportionately affected by inequities in perinatal outcomes, yet their perspectives are rarely centered in efforts to address these inequities. This research addresses health inequities by presenting a robust, transparent and participatory approach to priority setting as a way to better represent the perspectives of those who carry the greatest burden of health inequities in evidence syntheses. In our work, marginalized adolescent parents adapted published literature around the experience and consequences of social stigma on perinatal outcomes, shifting our understanding of root causes and possible solutions.
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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 | 0.500 | 0.533 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.031 | 0.018 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.010 | 0.030 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".