Dread and solace: Talking about perinatal mental health
Bibliographic record
Abstract
Perinatal mental health issues are a global public health challenge. Worldwide, it is estimated that 10% of pregnant women, and 13% of women who have just given birth, experience a mental disorder. Yet, for many reasons - including stigma, limited access to services, patients' lack of awareness about symptoms, and inadequate professional intervention - actual rates of clinical and subclinical perinatal mental health issues are likely higher. Studies have explored experiences such as postpartum depression, but few involve a wider-ranging exploration of a variety of self-reported perinatal mental health issues through personal narrative. We conducted 21 narrative interviews with women, in two Canadian provinces, about their experiences of perinatal mental health issues. Our aim was to deepen understanding of how individual and cultural narratives of motherhood and perinatal mental health can be sources of shame, guilt, and suffering, but also spaces for healing and recovery. We identified four predominant themes in women's narrative: feeling like a failed mother; societal silencing of negative experiences of motherhood; coming to terms with a new sense of self; and finding solace in shared experiences. These findings are consistent with other studies that highlight the personal challenges associated with perinatal mental health issues, particularly the dread of facing societal norms of the 'good mother'. We also highlight the positive potential for healing and self-care through sharing experiences, and the power of narratives to help shape feelings of self-worth and a new identity. This study adheres to the expectations for conducting and reporting qualitative research.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".