Best practices in prenatal health promotion: Perceptions, experiences, and recommendations of Ottawa, Canada, prenatal key informants
Bibliographic record
Abstract
Background:Prenatal health promotion includes the provision of evidence-based information and practical skills to optimize maternal–fetal outcomes. Increasingly, prenatal education is delivered by both healthcare professionals and allied childbirth educators, in community- or hospital-based group classes, targeted outreach programs, and online modules.Objectives:To better understand how prenatal health promotion relates to a diverse urban community, we assessed the perspectives of Ottawa, Canada prenatal key informants.Design:This is a qualitative research with key informant interviews.Methods:Semi-structured interviews were conducted with 11 prenatal key informants, responsible for the design, delivery, or promotion of publicly available prenatal health services. Interviews explored concepts and delivery of prenatal health promotion, strategies to address existing and emerging prenatal topics, identification of barriers to prenatal health services, and recommendations.Results:Key informants recommended a lifespan approach to prenatal health promotion, with an emphasis on healthy behaviors, emotional health, labor and delivery, and postpartum/early parenting. Recognizing community health disparities, key informants used community outreach, and intersectoral collaborations for Indigenous and other at-risk communities to mitigate barriers to prenatal service uptake.Conclusions:Ottawa key informants conceptualized prenatal health promotion as inclusive, comprehensive, and as an extension of preconception, school-based sexual education. Respondents recommended the design and delivery of prenatal interventions as culturally safe and trauma informed, using online modalities to complement in-person activities. The experience and intersectoral networks of community-based prenatal health promotion programs represent potential capacity to address emergent public health risks to pregnancy, particularly among at-risk populations.Plain Language Summary:A broad and diverse community of professionals deliver prenatal education to help people have healthy babies. We interviewed Ottawa, Canada experts in prenatal care/education to learn about the design and delivery of reproductive health promotion. We found that Ottawa experts emphasized healthy behaviors beginning before conception and through pregnancy. Community outreach was identified as a successful strategy to promote prenatal education to marginalized groups.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".