Postpartum Depression and Maternal-Infant Bonding Experiences in Social Media Videos: Qualitative Content Analysis
Bibliographic record
Abstract
Background: While the negative effects of postpartum depression on maternal-infant bonding are well-documented, our understanding of how it exerts these effects remains incomplete. A better understanding of how maternal postpartum depression affects bonding can enable clinicians to better identify and support mothers with difficulties bonding with their children. Objective: This study aims to describe the bonding experiences of mothers with postpartum depression through an analysis of short-form videos and user engagement. Methods: We collected publicly available highly-viewed TikTok videos using hashtags associated with postpartum depression and associated engagement metrics in May 2023. After manual screening, we extracted 533 videos related to the mother-infant bond, from which we analyzed a random subset of 159 videos. We abstracted categories from videos using a hybrid deductive and inductive approach. Negative binomial regression models of video likes, views, shares, and comment count were used with content categories and the creator's numbers of followers as independent variables. Results: Abstraction of content from mother-infant bond videos resulted in six categories: (1) navigating anxiety and anger, (2) creating physical and emotional boundaries, (3) overwhelmed by demands of caregiving, (4) subverted expectations, (5) enduring and finding strength through the challenge of postpartum depression, and (6) can't remember early life. Subverted expectations and navigating anxiety and anger categories were associated with increased views (rate ratio [RR] 1.72, 95% CI 1.22-2.43; RR 1.61, 95% CI 1.09-2.38, respectively), likes (RR 3.61, 95% CI 2.55-5.11; RR 3.96, 95% CI 2.69-5.85, respectively), shares (RR 2.95, 95%CI 2.09-4.18; RR 2.45, 95% CI 1.66-3.61, respectively), and comments (RR 2.78, 95% CI 1.97-3.94; RR 1.89, 95% CI 1.28-2.79, respectively). Sensitivity analysis with creators with fewer followers mostly aligned with these results. Conclusions: This qualitative content analysis of short-form videos identified specific ways postpartum depression impacts the mother-infant bond, highlighting strategies for clinicians to support bonding. Analysis of engagement metrics further demonstrated the types of experiences that most resonate with viewers. Our findings demonstrate the potential of this qualitative method to augment understanding of lived experiences.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".