Perceptions and experiences of perinatal mental disorders in rural, predominantly ethnic minority communities in northern Vietnam
Bibliographic record
Abstract
BACKGROUND: Preliminary research has suggested that perinatal mental disorders (PMDs), including post-partum depression, are prevalent in Vietnam. However the extent to which these disorders are recognized at the community level remains largely undocumented in the literature. PMDs have also never been investigated within Vietnam's significant ethnic minority populations, who are known to bear a greater burden of maternal and infant health challenges than the ethnic majority. OBJECTIVE: To investigate knowledge and perceptions of PMDs and their treatments at the community level in a rural, predominantly ethnic minority region of northern Vietnam. METHODS: Qualitative semi-structured interviews were conducted on the topic of common PMDs. Participant groups were primary health workers (PHWs) working at local community health centers, and pregnant or postpartum women enrolled in a program for maternal and infant health that was not mental health related. Interviews included vignette scenarios that asked respondents to interpret cases of women experiencing PMDs, as well as open-ended questions about mental disorders and their treatments. RESULTS: Twelve PHWs and 14 perinatal women completed the study. Major themes that emerged from the interviews included (1) Family relationships impact psychological well-being, (2) Nutrition contributes to perinatal mental health, (3) Both traditional and western medicine play roles in perinatal health, (4) There was a lack of personal experience with women experiencing PMDs, (5) Descriptions of mental health symptoms focused on behaviours, and (6) Community care is the primary mental health support. CONCLUSIONS: PHWs reported having almost never treated a woman with a PMD. However, anecdotal evidence from the women interviewed suggests that there are incidents of mental disorders during the perinatal period that go largely unaddressed. Willingness to present to primary care appears to be high, and presents an opportunity to address this need by training PHWs in effective screening, treatment, and referral. Such training should account for culturally specific presentations of mental disorders as well as the importance of the patient's social context. To the best of the author's knowledge, this research presents the first evidence of a PMD burden within Vietnam's ethnic minority communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".