Does Love Matter to Infants' Health: Influence of Maternal Attachment Representations on Reports of Infant Health
Bibliographic record
Abstract
Although there is considerable support for the influence of maternal attachment on children's development (see Gerhardt, 2015), this is one of the first studies to examine the effects of maternal prenatal reports of attachment representations with close others on reports of infants’ health. Mothers (N = 483) completed surveys to assess attachment and depression in the second or third trimester of pregnancy, infants’ health over the first 6 months, and depression and infant temperament when infants were 6 months old. We found that insecure mothers, as compared to secure mothers, were more likely to report that their infants experienced colic and illnesses associated with immune, cardiovascular, and respiratory systems. It may be that secure mothers experience less anxiety associated with parenting and, as expected, were consistently found to report lower levels of infant illness symptoms. Alternatively, secure mothers would be expected to provide more consistent and responsive care compared to insecure mothers, which may also influence their infants’ physical health (see also Gerhardt, 2015). Future research needs to further explore this finding — do secure mothers simply perceive their infants to be healthier due to their own low anxiety or are infants of secure mothers healthier due to consistent and responsive care received?
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".