Maternal alexithymia and caregiving behavior: the role of executive functioning - A FinnBrain Birth Cohort study
Bibliographic record
Abstract
PURPOSE: The growing interest in parental cognition calls for research clarifying how cognition interacts with other parenting determinants to shape caregiving behavior. We studied the interplay between executive functioning (EF; cognitive processes that enable goal-directed thinking and behavior) and alexithymic traits (characterized by emotion processing/regulation difficulties) in relation to emotional availability (EA; the dyad's ability to share an emotionally healthy relationship). As EF has been reported to shape parents' ability to regulate thoughts and emotions during caregiving, we examined whether EF moderated the association between maternal alexithymic traits, and EA. METHODS: Among 119 mothers with 2.5-year-olds drawn from the FinnBrain Birth Cohort, EF was measured with Cogstate tasks, alexithymic traits with the Toronto Alexithymia Scale (TAS-20), and caregiving with the Emotional Availability Scales (EAS). RESULTS: = 0.03, p = .06). These associations weakened slightly when controlling for education level. Estimation of simple slopes and a Johnson-Neyman figure indicated a significant association between higher EOT and lower EAS, that increased in strength as EF decreased from the group mean level. CONCLUSIONS: The influence of cognitive alexithymic traits on EA could be especially pronounced among low EF parents, but further studies are needed to support and extend the findings. The potential role of parental reflective functioning in this context is discussed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".