Maternal prenatal felt security and infant health at birth interact to predict infant fussing and crying at 12 months postpartum.
Bibliographic record
Abstract
Infants born with medical problems are at risk for less optimal developmental outcomes. This may be, in part, because neonatal medical problems are associated with maternal distress, which may adversely impact infants. However, the reserve capacity model suggests that an individual's bank of psychosocial resources buffers the adverse effects of later-encountered stressors. This prospective longitudinal study examined whether preexisting maternal psychosocial resources, conceptualized as felt security in close relationships, moderate the association between neonatal medical problems and infant fussing and crying 12 months postpartum. Maternal felt security was measured by assessing its indicators in 5,092 pregnant women. At birth, infants were classified as healthy or having a medical problem. At 12 months, experience sampling was used to assess daily maternal reports of fussing and crying in 135 mothers of infants who were healthy or had medical problems at birth. Confirmatory factor analyses revealed that attachment, relationship quality, self-esteem, and social support can be conceptualized as indicators of a single felt security factor. Multiple regression analyses revealed that prenatal maternal felt security interacts with infant health at birth to predict fussing and crying at 12 months. Among infants born with medical problems, higher felt security predicted decreased fussing and crying. Maternal felt security assessed before birth dampens the association between neonatal medical problems and subsequent infant behavior. This supports the hypothesis that psychosocial resources in reserve can be called upon in the face of a stressor to reduce its adverse effects on the self or others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".