Enablers and barriers to seeking help for a postpartum mood disorder
Bibliographic record
Abstract
Objective: To explore the barriers and enablers identified by women experiencing a postpartum mood disorder (PPMD) that both preclude and facilitate their help-seeking behaviours for this often devastating illness. Design: A qualitative study using a grounded theory approach Setting: Well-Baby Clinics offered through the Public Health Department, Early Years Centres, Mothercraft and a Parent Resource Centre in a large Canadian city. Participants: Ten women who had either been formally diagnosed as having a PPMD or who self-identified as experiencing a constellation of symptoms indicative of a PPMD Methods: Interviews that were transcribed verbatim and analyzed using a grounded theory approach as described by Strauss and Corbin (1998). Results: The core category of ‘Having postpartum’ captured the essence of women’s experiences in seeking help for a PPMD. Women identified four main stressors that contributed to their development of a PPMD, two barrier categories and an enabler category which influenced their help-seeking behaviours. Through navigation of both formal and informal help, women were able to begin the journey to reclaim the mothering soul they had lost to mental illness. Conclusions: Pregnancy, birth and becoming a mother collectively represent a critical period of physical and emotional upheaval in a woman’s life. The need for a holistic care approach that supports the emotional and physical health of the dyad is imperative.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".