Suffering in silence: Accessing mental health care and repetitive transcranial magnetic stimulation (rTMS) for peripartum depression - A qualitative study
Bibliographic record
Abstract
Peripartum depression (PPD) is a prevalent and serious mental health disorder that is often underdiagnosed and undertreated due to limited effective and safe treatment options. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a non-invasive treatment for PPD, yet awareness among patients is low. This study aims to identify barriers and facilitators to accessing mental health treatment, particularly rTMS, for PPD. We conducted 36 interviews with individuals who experienced depressive symptoms during the peripartum period and health providers, followed by a descriptive interpretive thematic analysis. Key risk factors identified include personal (i.e., age), clinical (i.e., traumatic birth), situational (i.e., COVID-19, homelessness), and social (i.e., discrimination, domestic abuse). Five themes emerged regarding barriers and facilitators: 1) the need for mom-centered care, 2) systemic challenges, 3) the importance of mental health education, 4) stigma and custody concerns, and 5) challenges in accessing care. Eighty-three percent of participants were unaware of rTMS, but following a brief description, 75% were willing to receive or refer to rTMS if it was available to them. Addressing systemic and access-related concerns is crucial to ensuring patients with PPD have access to safe, effective, and accessible treatments.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".