Subjective well-being across partnerships.
Bibliographic record
Abstract
Drawing on data gathered from 554 focal participants in the German Family Panel (pairfam) study surveyed at 4 time points spanning 2 intimate unions, this brief report investigated changes in 3 indicators of subjective well-being (life satisfaction, depressive symptoms, and self-esteem) across partnerships. Latent change score-modeling results showed no mean-level changes in life satisfaction or self-esteem from Time 1 in Partnership 1 to Time 2 of Partnership 2 and a slight increase in depressive symptoms across partnerships. This overall stability in subjective well-being was evident despite a series of changes in the interim period: Subjective well-being worsened as the end of Partnership 1 approached, improved after the initiation of Partnership 2, and leveled off as Partnership 2 progressed. Being female predicted worse initial subjective well-being at the outset of the study, a greater decrease in well-being as participants approached the end of Partnership 1, and an overall reduction in self-esteem and increase in depression symptoms across partnerships. Being older, married in Partnership 1, and having a longer duration first partnership predicted worse initial well-being, a steeper decrease in life satisfaction as Partnership 1 drew to a close, and older participants had lower life satisfaction across unions. These findings add to a growing literature documenting the remarkable stability of individual and relational functioning across time while also highlighting those most at risk of reduced subjective well-being across partnerships. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".