Impacts of online and group perinatal education: a mixed methods study protocol for the optimization of perinatal health services
Bibliographic record
Abstract
BACKGROUND: Prenatal education is a core component of perinatal care and services provided by health institutions. Whereas group prenatal education is the most common educational model, some health institutions have opted to implement online prenatal education to address accessibility issues as well as the evolving needs of future parents. Various studies have shown that prenatal education can be effective in acquisition of knowledge on labour and delivery, reducing psychological distress and maximising father's involvement. However, these results may depend on educational material, organization, format and content. Furthermore, the effectiveness of online prenatal education compared to group prenatal education remains unclear in the literature. This project aims to evaluate the impacts of group prenatal education and online prenatal education on health determinants and users' health status, as well as on networks of perinatal educational services maintained with community-based partners. METHODS: This multipronged mixed methods study uses a collaborative research approach to integrate and mobilize knowledge throughout the process. It consists of: 1) a prospective cohort study with quantitative data collection and qualitative interviews with future and new parents; and 2) a multiple case study integrating documentary sources and interviews with stakeholders involved in the implementation of perinatal information service networks and collaborations with community partners. Perinatal health indicators and determinants will be compared between prenatal education groups (group prenatal education and online prenatal education) and standard care without these prenatal education services (control group). DISCUSSION: This study will provide knowledge about the impact of online prenatal education as a new technological service delivery model compared to traditional group prenatal education. Indicators related to the complementarity of these interventions and those available in community settings will refine our understanding of regional perinatal services networks. Results will assist decision-making regarding service organization and delivery models of prenatal education services. PROTOCOL VERSION: Version 1 (February 9 2018).
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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 | 0.073 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".