Alexithymic traits and parental postpartum bonding: Findings from the FinnBrain Birth Cohort Study
Bibliographic record
Abstract
In the postpartum period, some parents experience problems in bonding with the infant, which can lead to difficulties in adjusting to the parental caregiving role. Alexithymia, through deficits in emotional processing, could potentially be associated with problems in parental postpartum bonding. In the current study, this association has been explored in a large population-based sample of mothers and fathers, and to our knowledge, this is the first study to investigate this association. The study population (n = 2,671) was part of the FinnBrain Birth Cohort study and included 1,766 mothers and 905 fathers who returned The Postpartum Bonding Questionnaire (PBQ) at three months postpartum and the 20-item Toronto Alexithymia Scale (TAS-20) at six months postpartum. Correlation analyses and hierarchical regression modeling, adjusted for selected background factors, were performed separately for mothers and fathers. The alexithymia dimension "Difficulty Identifying Feelings" (DIF) in mothers and fathers, and additionally dimensions of "Difficulty Describing Feelings" (DDF) and "Externally Oriented Thinking" (EOT) in fathers were associated with weaker postpartum bonding, when related background factors were controlled for. To our knowledge this was the first study to investigate the relationship between parents' alexithymic traits and postpartum bonding within a large birth cohort study population. The main finding was that especially higher levels of maternal DIF and paternal EOT were associated with weaker postpartum bonding. Longitudinal studies are needed to establish the potential causality of this relationship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".