Treating maternal depression: understanding barriers and facilitators to repetitive transcranial magnetic stimulation treatment in Canada-a protocol
Bibliographic record
Abstract
Background: Peripartum depression (PPD) is a serious public health issue associated with severe and potentially long-term adverse maternal and child developmental outcomes. Suicide and overdose, for example, accounts for up to a third of maternal deaths. A current depression diagnosis with no active treatment is a common risk factor for maternal suicide. Repetitive transcranial magnetic stimulation (rTMS) is a non-pharmacological treatment that has recently shown some promise as an effective treatment with limited side effects for PPD, but more research is required. This study aims to identify current barriers and potential facilitators for women with PPD accessing treatment in general, and rTMS specifically. Methods: This study will consist of two anonymous, self-administered surveys, focus groups, and interviews. A descriptive interpretative approach will be employed, and thematic analysis will be completed for the focus groups and interviews. Participants who are currently, or have previously experienced depressive symptoms, as well as health providers will be recruited. Our study will follow an equity, diversity, and inclusion (EDI) perspective on sex, gender, and ethnicity and the gender-based analysis plus (GBA+) analytic tool will be used. Both a qualitative and quantitative analysis of the data will be conducted. Discussion: We expect to find education and accessibility to be primary treatment barriers for persons with PPD. Identifying and addressing barriers is a critical first step towards the devolvement of initiatives that can work towards improving mental health in this population.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".