Exploring Maternal Mental Health in Syrian Refugee Women
Bibliographic record
Abstract
Introduction There has been a rapid influx of 30,000 Syrian refugees in Canada, many are women of childbearing age, and most have young children. The literature reports that refugee women are almost 5 times more likely to develop postpartum depression than Canadian-born women. However, little is known about the experiences that the Syrian refugee women have encountered pre- and post-resettlement and their perceptions of mental health issues in general, and of maternal depression in particular. Thus, there is an urgent need to understand the refugee women's experiences of having a baby in Canada from a mental health perspective. Methods Participants include Syrian refugee women who migrated to Saskatoon Canada in 2015–16 and who were either pregnant or up to one year postpartum. Qualitative data was collected via a focus group with thematic analysis, while depression with Edinburgh Postnatal Depression Scale (EPDS) and PTSD screening and sociodemographic descriptive data were collected from a structured questionnaire to provide context for the qualitative analysis. Results Twelve women participated in the focus group, despite smiling often, 58% of them screened as probable depression (EPDS > 10), 25% screened positive for depression (> 12 on EPDS), and 17% screened positive for PTSD. None of the women indicated intimate partner violence or suicidal thoughts. All participants indicated social support, mostly partner, and 25% had a history of depression. Thematic analysis will be shared. Conclusions Perinatal Depression is a serious problem for refugee women that deserve more in-depth study to ensure optimal outcomes and to develop services and programs. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".