Factors affecting the emotional wellbeing of women and men who experience miscarriage in hospital settings: a scoping review
Bibliographic record
Abstract
BACKGROUND: Miscarriage can be a devastating event for women and men that can lead to short- and long-term emotional distress. Studies have reported associations between miscarriage and depression, anxiety, and post-traumatic stress disorder in women. Men can also experience intense grief and sadness following their partner's miscarriage. While numerous studies have reported hospital-related factors impacting the emotional wellbeing of parents experiencing miscarriage, there is a lack of review evidence which synthesises the findings of current research. AIMS: The aim of this review was to synthesise the findings of studies of emotional distress and wellbeing among women and men experiencing miscarriage in hospital settings. METHODS: A systematic search of the literature was conducted in October 2020 across three different databases (CINAHL, MEDLINE and PsycInfo) and relevant charity organisation websites, Google, and OpenGrey. A Mixed Methods appraisal tool (MMAT) and AACODS checklist were used to assess the quality of primary studies. RESULTS: Thirty studies were included in this review representing qualitative (N = 21), quantitative (N = 7), and mixed-methods (N = 2) research from eleven countries. Findings indicated that women and men's emotional wellbeing is influenced by interactions with health professionals, provision of information, and the hospital environment. Parents' experiences in hospitals were characterised by a perceived lack of understanding among healthcare professionals of the significance of their loss and emotional support required. Parents reported that their distress was exacerbated by a lack of information, support, and feelings of isolation in the aftermath of miscarriage. Further, concerns were expressed about the hospital environment, in particular the lack of privacy. CONCLUSION: Women and men are dissatisfied with the emotional support received in hospital settings and describe a number of hospital-related factors as exacerbators of emotional distress. IMPLICATIONS FOR PRACTICE: This review highlights the need for hospitals to take evidence-informed action to improve emotional support services for people experiencing miscarriage within their services.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".