“How can I seek a consultation if I don’t have a high fever ?”: Barriers to Mental Healthcare Access for Women in the Perinatal Period in Rwanda
Bibliographic record
Abstract
Background: Literature highlights barriers to mental healthcare access in the perinatal period, but none specific to Rwanda. The unique historical context of the genocide against the Tutsi may present distinct challenges. This study aimed to identify these barriers in Rwanda. Methods: This study employed a qualitative interpretive descriptive approach as part of a multimethod investigation. Four focus group discussions were conducted with 31 perinatal women, and 32 individual interviews were conducted with healthcare providers, including community health workers. Data were analysed thematically. Results: Barriers were identified at multiple levels. At the individual level, barriers included low literacy about perinatal mental health symptoms, minimizing negative experiences, fear of being stigmatized, ignorance about the availability of mental health services in the perinatal period, and economic challenges. Family and social-cultural barriers included stigmatization of people with mental health problems, minimization of what happened by friends and family, and lack of support from partners and friends. Institutional and structural barriers included limited services, misdiagnosis, heavy workloads, staff unawareness, and lack of training and guidelines for screening and reporting. Conclusion: This study identified barriers to perinatal mental healthcare at individual, family and social-cultural, institutional and structural levels. Addressing these barriers requires targeted strategies to improve perinatal mental healthcare access across all identified levels.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".