Experiences and long-term repercussions of perinatal grief in women after perinatal bereavement: a meta-ethnography
Bibliographic record
Abstract
Introduction Perinatal bereavement can profoundly disrupt maternal identity and is often accompanied by longer-term emotional suffering. Whilst immediate grief responses have been studied, less is known about how this experience evolves over time. This meta-ethnography aimed to synthesize qualitative evidence on the long-term experiences and repercussions of perinatal grief in women after a pregnancy loss. Methods A systematic review of six databases was conducted. Primary qualitative studies were included if they addressed experiences occurring at least one year after a perinatal bereavement. A total of 2,253 records were screened, and 18 studies met the inclusion criteria. Data quality was assessed, and the data were subjected to an analytic synthesis using meta-ethnography. Results Three themes and six sub-themes were identified, revealing perinatal grief as a prolonged and transformative experience. Women reported emotional pain, identity disruption, and social silencing. In contrast, empathic care, sustained support, and social validation helped them reconstruct their identities. In line with meta-ethnographic approaches, a theory was developed: “The quietest of births cause the loudest anguish: Whilst some bereaved mothers walk a solitary path, those with broader support networks are more empowered, but both experience an intense change to The Self.” . Discussion These findings show grief is shaped not only by the loss itself but also by how it is acknowledged or silenced by healthcare systems and society. Gaps were identified regarding long-term grief during times of health system uncertainty and in cases of fetal malformation, revealing the need for further research and policy development. Conclusion Supportive and continuous care between lost and future pregnancies is essential to alleviate suffering and promote identity reconstruction among bereaved mothers facing long-term perinatal grief.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".