Engaging Men in Prenatal Health via eHealth: Findings From a National Survey
Bibliographic record
Abstract
BACKGROUND: Pregnancy outcomes in the United States rank among the worst of countries with a developed health care system. Although traditional prenatal health primarily focuses on women, promising findings have emerged in international research that suggest the potential of including men in prenatal health interventions in the United States. eHealth apps present a promising avenue to reach new and expectant fathers with crucial parenting knowledge and healthy, supportive behaviors. OBJECTIVE: The aim was to explore the perceived role of men in prenatal health, acceptability of eHealth to positively engage men during pregnancy, and participant-suggested ways of improving a prenatal health app designed for new and expectant fathers. METHODS: A nationally representative sample of adult males (N=962) was recruited through an online survey panel. A third-party market research and digital data collection agency managed the recruitment. The sample had a mean age of 30.2 (SD 6.3) years and included both fathers (413/962, 42.9%) and non-fathers (549/962, 57.1%). Nearly 12.0% (115/962) of participants had a partner who was pregnant at the time of the survey. RESULTS: Despite perceived barriers, such as time constraints, financial burdens, and an unclear role, men believe it is important to be involved in pregnancy health. The majority of participants (770/944, 81.6%) found the site to contain useful and interesting information. Most substantially, more than three-quarters (738/962, 76.7%) of the sample said they would share the site with others who would benefit from the information. Participants recommended the addition of interactive modules, such as a financial planning tool and videos, to make the site stronger. CONCLUSIONS: We explored the use of targeted eHealth to introduce men to prenatal education. Results indicate men are favorable to this intervention. Additional refinement should include interactive tools to further engage men in this important issue. Reaching men at the prenatal phase is an early "teachable moment"-where new/expectant fathers are open to information on how to help their partners have a healthy pregnancy and promote the health of their unborn children. Findings will further inform best practices for engaging men in pregnancy, which is crucial for improving maternal and child health outcomes in the United States.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".