Demographic and Support Interest Differences Among Nonbirthing Parents Using a Digital Health Platform With Parenthood-Related Anxiety: Cross-Sectional Study
Bibliographic record
Abstract
Background: The transition to parenthood is a period of major stressors and increased risk of anxiety for all parents. Though rates of perinatal anxiety are similar among women (4%-25%) and men (3%-25%), perinatal anxiety research on nonbirthing partners remains limited. Objective: We aimed to examine whether demographic characteristics or digital perinatal support preferences differed among nonbirthing partners with compared to without self-reported high parenthood-related anxiety. Methods: In this large cross-sectional study of nonbirthing partners using a digital perinatal health platform during their partner's pregnancy, users reported their parenthood-related anxiety through a 5-item Likert scale in response to the prompt "On a scale of 1=None to 5=Extremely, how anxious are you feeling about parenthood?" High parenthood-related anxiety was defined as reporting being very or extremely anxious about parenthood. During the onboarding survey, in response to the question "Which areas are you most interested in receiving support in?" users selected as many support interests as they desired from a list of options. Chi-square and Fisher exact tests were used to compare demographic characteristics and support interests of nonbirthing partners with low versus high parenthood anxiety. Logistic regression models estimated the odds ratios (ORs), with 95% CIs, of high parenthood-related anxiety with each user characteristic or digital support interest. Results: Among 2756 nonbirthing partners enrolled in the digital platform during their partner's pregnancy, 2483 (90.1%) were men, 1668 (71.9%) were first-time parents, 1159 (42.1%) were non-Hispanic White, and 1652 (50.9%) endorsed an annual household income of >US $100,000. Overall, 2505 (91.9%) reported some amount of parenthood-related anxiety, and 437 (15.9%) had high parenthood-related anxiety. High parenthood-related anxiety was more common among non-White nonbirthing partners: compared to those who identified as non-Hispanic White, those who identified as Asian, Black, or Hispanic had 2.39 (95% CI 1.85-3.08), 2.01 (95% CI 1.20-3.23), and 1.68 (95% CI 1.15-2.41) times the odds of high parenthood-related anxiety, respectively. Lower household income was associated with increased odds of reporting high parenthood anxiety, with the greatest effect among those with annual incomes of US $100,000 (OR 2.13, 95% CI 1.32-3.34). In general, nonbirthing partners were interested in receiving digital support during their partner's pregnancy, but those with high parenthood-related anxiety were more likely to desire digital support for all support interests compared to those without high parenthood anxiety. Those with high parenthood-related anxiety had more than 2 times higher odds of requesting digital education about their emotional health compared to those without high parenthood-related anxiety (OR 2.06, 95% CI 1.67-2.55). Conclusions: These findings demonstrate the need for perinatal anxiety-related support for all nonbirthing partners and identify nonbirthing partners' demographic characteristics that increase the odds of endorsing high parenthood-related anxiety. Additionally, these findings suggest that most nonbirthing partners using a digital health platform with high parenthood-related anxiety desire to receive perinatal mental health support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".