Psychologic and obstetric predictors of couples' grief during pregnancy after miscarriage or perinatal death
Bibliographic record
Abstract
OBJECTIVE: To determine if the psychologic constructs of self-criticism and marital adjustment, considered jointly with obstetric and demographic factors, are significant predictors of grief during a pregnancy after a miscarriage or perinatal death. METHODS: Participants included 60 pregnant women with previous miscarriages or perinatal deaths, and 50 of their partners. Participants completed a package of psychometric instruments between the tenth and 19th week of gestation. Predictors of grief (active grief, difficulty coping, despair) included (1) psychologic factors: marital adjustment and self-criticism; (2) demographic factors: age and number of living children; and (3) obstetric factors: gestational age at time of loss, number of losses, and time between loss and subsequent conception. RESULTS: Stepwise regression analyses were conducted for each grief component for women and men. For women, active grief was significantly associated with high self-criticism and later losses (R(2) = 0.31). Later losses and longer time between loss and conception were significantly associated with difficulty coping (R(2) = 0.55) and despair (R(2) = 0.44). In men, active grief was associated with high self-criticism and later losses (R(2) = 0.28), difficulty coping (R(2) = 0.18), and despair (R(2) = 0.25) with high self-criticism. A trend was found for poor marital adjustment to be associated with higher levels of difficulty coping and despair in men. CONCLUSION: High levels of self-criticism and later gestational age at time of loss are predictors of increased grief during a pregnancy after a miscarriage or perinatal death. Increased time between loss and subsequent conception is also predictive of increased grief for women. For men, low levels of marital adjustment are predictive of increased grief. These results may be helpful in counselling couples considering pregnancy after a loss.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".