Postpartum Mental Health and Perceptions of Discrimination Among Asian Fathers During the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE: The purpose of this study was twofold: (1) to examine the prevalence of postpartum depression and anxiety symptomatology among fathers of Asian descent living in North America during the COVID-19 pandemic, and (2) to identify the occurrences of online racial discrimination. STUDY DESIGN AND METHODS: Using a cross-sectional design and convenience sampling methods, we recruited fathers online via social media sites (Facebook, Instagram) between March 12 and July 31, 2022. The Edinburgh Postnatal Depression Scale, General Anxiety Scale, and the Online Victimization Scale assessed mental health well-being and discrimination outcomes. Data were analyzed using descriptive statistics, two sample t-test, chi-square test of independence, and Pearson's correlation analysis. RESULTS: Our sample included 61 fathers within 6 months postpartum living in the United States and Canada. Participants were on average 34 years old, married, and represented 17 Asian ethnic groups, including Asian Indian (41%), Filipino (11.3%), and Korean (8.1%). One-third of our participants (31.1%, n = 19) were at high risk of developing postpartum depression and scores of three (4.9%) fathers indicated they had clinically significant anxiety. Overall, 26.3% reported experiencing direct online racial discrimination and 65% reported occurrences of indirect online racial discrimination. CLINICAL IMPLICATIONS: There was a high rate of depressive symptoms and occurrences of online racial discrimination among fathers of Asian descent living in North America. These rates are higher than the general perinatal population and further research is warranted to examine risk factors and preventive strategies among this unique paternal ethnic group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".