Protective and risk factors for women’s mental health after a spontaneous abortion
Bibliographic record
Abstract
OBJECTIVE: to examine personal and contextual protective and risk factors associated with women's mental health after a spontaneous abortion. METHOD: a cross-sectional study was carried out where 231 women who had experienced spontaneous abortions in the past 4 years answered a self-reporting online questionnaire to assess their mental health (symptoms of depression, anxiety, perinatal grief) and to collect personal as well as contextual characteristics. RESULTS: women who had experienced spontaneous abortions within the past 6 months had higher scores for depressive symptoms than those who had experienced spontaneous abortions between 7 and 12 months ago, while anxiety level and perinatal grief did not vary according to the time since the loss. Moreover, low socioeconomic status, immigrant status, and childlessness were associated with worse mental health after a spontaneous abortion. In contrast, the quality of the conjugal relationship and the level of satisfaction with health care were positively associated with women's mental health. CONCLUSION: women in vulnerable situations, such as immigrants, women with a low socioeconomic status, or childless women are particularly vulnerable to mental health problems after a spontaneous abortion. However, beyond those personal and contextual factors, the quality of the conjugal relationship and the level of satisfaction with health care could be important protective factors.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".