Relationship satisfaction as a moderator of stress and physical health outcomes during the transition to parenthood
Bibliographic record
Abstract
The transition to parenthood is marked by an increase in stress for both parents as it is one of the most dramatic and intense transitions in the family life cycle (Martins, 2019). The changes in roles and relationships of these individuals, and the stressors that it brings, can affect the quality of both individuals’ health as they become parents (Martins, 2019). It has been noted that close relationships affect immune function, inflammation, and other health outcomes (Kiecolt-Glaser et al., 2010). This could be explained by social baseline theory, which states that social relationships confer energy-saving benefits due to the decreased risks and increased regulatory efforts of being in an interpersonal relationship (Beckes & Coan, 2011). Therefore, perhaps the quality of the intimate relationship during the transition to parenthood might moderate the demonstrated association between stress levels and physical health outcomes. Thus, I hypothesize that the quality of the relationship between romantic partners during the transition to parenthood might moderate the association between perceived stress levels and physical health outcomes. Since relationship satisfaction might serve as a buffer moderating the association between perceived stress and physical health outcomes, an exploratory analysis was also conducted to see what might be associated with relationship satisfaction. Literature has found that relationship satisfaction is affected by emotional flooding, which is the subjective experience of being overwhelmed by a partner's negative affect, finding it to be unexpected and intense, and feeling as though one's information processing is impaired (Gottman, 1993). Increased emotional flooding experienced by one partner has led to less satisfaction within the romantic relationship (Walker et al., 2009). However, the literature has mixed findings on the mean levels of flooding in certain interpersonal relationships. For example, Foran et al. (2020) reported that men reported slightly higher mean levels of flooding than women, which may be due to men having somewhat greater reactivity to anger displays of their partners or gender differences in learned reactions to conflict. Del Vecchio et al. (2016) found that in caregiver-child interactions, mothers' flooding was significantly higher than fathers' flooding. Therefore, I wanted to test if there were gender differences in reported flooding levels in the data set analyzed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".