Paternal Perinatal Experiences during the COVID-19 Pandemic: A Framework Analysis of the Reddit Forum Predaddit
Bibliographic record
Abstract
During the COVID-19 pandemic, new parents were disproportionately affected by public health restrictions changing service accessibility and increasing stressors. However, minimal research has examined pandemic-related stressors and experiences of perinatal fathers in naturalistic anonymous settings. An important and novel way parents seek connection and information is through online forums, which increased during COVID-19. The current study qualitatively analyzed the experiences of perinatal fathers from September to December 2020 through the Framework Analytic Approach to identify unmet support needs during COVID-19 using the online forum predaddit on reddit. Five main themes in the thematic framework included forum use, COVID-19, psychosocial distress, family functioning, and child health and development, each with related subthemes. Findings highlight the utility of predaddit as a source of information for, and interactions of, fathers to inform mental health services. Overall, fathers used the forum to engage with other fathers during a time of social isolation and for support during the transition to parenthood. This manuscript highlights the unmet support needs of fathers during the perinatal period and the importance of including fathers in perinatal care, implementing routine perinatal mood screening for both parents, and developing programs to support fathers during this transition to promote family wellbeing.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".