A systematic review and thematic synthesis of women’s coping strategies for early to late miscarriage
Bibliographic record
Abstract
BACKGROUND: Miscarriage is a common adverse pregnancy outcome that can have long-term negative impacts on women. Coping strategies can indicate how someone attempts to manage their response to a stressor and may provide an indication of how to support them after a traumatic event. Despite emerging qualitative research on miscarriage experiences, there has been no comprehensive synthesis focusing on how women cope with miscarriage. AIM: This study aims to explore women's experiences of coping with a miscarriage without a known cause occurring up to 24 weeks gestation. METHODS: A double-blinded systematic review and thematic synthesis of qualitative papers was conducted. A search of seven databases, supplemented with a manual search was carried out. Articles published in academic journals between 2014 and 2024 were included. Papers were critically appraised using two tools, analysed thematically and critically discussed through a thematic synthesis. FINDINGS: Nine studies conducted across eight countries published between 2016 and 2024 were included. Four themes were identified: Denying or Commemorating the Lost Child, Changed Outlook after the Loss, Seeking a Cause or Meaning, and Social Interactions as a Source of Support or Pain. These themes demonstrate that women cope with miscarriage in diverse ways but with some similarities across different contexts. CONCLUSIONS: The impact of miscarriage is felt by women cross-culturally. Coping strategies after miscarriage are both highly individualised and deeply interconnected with social attitudes. Maternity and healthcare professionals should consider how their support and response has the potential to empower or further impact the experience of miscarriage.
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 | 0.037 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".