Maternal Perinatal Depression, Alexithymia, and Couple Functioning: Which Relationship Exists with Prenatal Attachment
Bibliographic record
Abstract
BACKGROUND: Prenatal attachment refers to the affective investment that parents develop towards their unborn baby during pregnancy. Studies have identified depressive symptoms, affect dysregulation, and poor marital adjustment as potential risk factors for poor prenatal attachment. However, no research has concurrently examined these factors. This study aims to explore the simultaneous impact of depressive symptomatology, alexithymia, and couple functioning on prenatal attachment to develop a more comprehensive understanding of the factors shaping the emotional bond between expectant mothers and their fetuses. METHODS: This cross-sectional study involved 344 women (mean age = 34.1, SD = 4.6) in their last trimester of pregnancy recruited from the National Health System. The participants completed the Edinburgh Postnatal Depression Scale, the Twenty-Item Toronto Alexithymia Scale, the Dyadic Adjustment Scale, and the Maternal Antenatal Attachment Scale. RESULTS: Regression analyses indicated that perinatal depression and alexithymia negatively affected the Quality of Prenatal Attachment, while Dyadic Cohesion served as a protective factor. CONCLUSIONS: The results emphasize the need for early identification of perinatal depression and alexithymia, along with targeted interventions aimed at supporting Dyadic Cohesion during pregnancy. These efforts are crucial for fostering positive prenatal attachment and enhancing maternal mental health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".