Network analysis and comparison of psychological distress among women with miscarriage experience
Bibliographic record
Abstract
As many as 15.3% of pregnancies do not end in a live birth meaning miscarriage is among the most common pregnancy complications. This experience is robustly evidenced to be associated with adverse health and well-being outcomes for mothers, however, much existing evidence focuses on psychopathology outcomes such as depression and anxiety disorders. Adopting a holistic approach inclusive of more nuanced mental health outcomes of pregnancy loss, this study sought to assess and compare distress among women experiencing miscarriage at home or in hospital. The current study applied Network Analysis to examine associations between indicators of psychological distress (e.g. feelings of loss, isolation, and devastation) in a sample of 839 women. Networks were assessed to identify the most influential indicators for the total sample of women who experienced miscarriage in the previous 5 years (N = 839), and for subsamples who reported experiencing management of their miscarriage at home (n = 493), or in hospital (n = 273). Results highlighted the most influential distress indicators in the network to be: ‘feelings of a person lost’, ‘destroyed zest for life’, and ‘feelings of isolation’. Comparison between subgroups, those who experienced miscarriage at home and in hospital, revealed similar network structures. Those who experienced miscarriage at home displayed greater global association between nodes in the network, i.e. stronger connections between distress indicators suggesting these have greater influence on each other a potentially exacerbate distress. The most influential distress indicators are highlighted as important targets for screening psychological distress, and as potentially viable intervention targets to promote greater well-being among women experiencing miscarriage, regardless of setting. These findings provide a novel understanding of psychological distress following miscarriage as a system of connected symptoms, further research is called for to examine broader influences of the impact of miscarriage network.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".