From villages to floating islands: A qualitative study exploring maternal experiences of support and isolation in Northern Sri Lanka
Bibliographic record
Abstract
Perinatal stress is a well-established risk factor for maternal and child health outcomes. Pregnant women in conflict-affected countries experience unique traumatic stressors, including displacement, material loss, loss of loved ones, and other war-related stressors. This qualitative study examines maternal mental health and parenting practices in Northern Sri Lanka, a region impacted by decades of civil war and displacement. Participants included 34 mothers who were pregnant at the time of the study, have one or more young children, and resided in the Northern Province during the conflict in Sri Lanka. The study explores the interrelationship between early adversity, chronic stressors, and maternal well-being. Qualitative insights highlight themes of unsupported resilience, emotion regulation amidst chronic stressors, and the erosion of traditional community support systems. Mothers reported navigating isolation within nuclear family structures, compounded by societal pressures and diminished communal trust. Despite these challenges, participants emphasized the importance of parenting values rooted in resilience and fostering an appreciation for overcoming hardship as they raise their own children. The study underscores the critical need for culturally sensitive, family-centered interventions that address maternal mental health and support parenting in post-conflict settings. These findings provide a foundation for tailoring maternal and child health policies to the unique needs of war-affected communities in Sri Lanka and beyond.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".