Grief and Bereavement Support for Parents in Low- or Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: The death of a child may be the most traumatic event a family can experience. Bereavement care for parents is essential for their physical and mental well-being and is a psychosocial standard of care. Childhood mortality is higher in low- or middle-income countries (LMICs); however, little is known regarding bereavement support or interventions for parents in LMICs. AIM: To identify programs, services, initiatives, or interventions offered to bereaved parents in LMICs in hospital settings. METHODS: A systematic search was executed following the Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles from LMICs describing interventions, programs, or resources provided to parents after the death of a child (0-18 years old) from any cause were included. Extracted data was categorized by demographics, study design, outcomes, and quality assessment using the McGill Mixed Methods Appraisal Tool (MMAT). RESULTS: We retrieved 4428 papers and screened their titles and abstracts, 36 articles were selected for full-text assessment, resulting in nine articles included in the final analysis. Most interventions described support for parents whose child died during the prenatal or neonatal period. The primary interventions included psychological counseling, creating mementos (such as photographs or footprints), and bereavement workshops. Only one paper described a fully established bereavement program for parents. Eight of the papers met high-quality criteria. DISCUSSION: Although bereavement care is crucial for parents whose child has died, only a few studies have documented bereavement interventions in LMICs. More research may help with bereavement program implementation and improved care for bereaved parents in LMICs.
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 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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".