The interpersonal benefits of goal adjustment capacities: the sample case of coping with poor sleep in couples
Bibliographic record
Abstract
Introduction This study examined the role of goal adjustment capacities and coping in the association between spousal sleep efficiency and relationship satisfaction in romantic couples. Method A community lifespan sample of 113 heterosexual couples (age range = 21–82 years) was recruited using newspaper advertisements in the Greater Montreal Area from June 2011 to December 2012. Participants completed study measures (i.e., Goal Adjustment Scale, Brief Pittsburgh Sleep Quality Index, Relationship Assessment Scale, and the Brief Cope) at two time points, ~1 year apart. Results The results of actor-partner interdependence models with moderation (MIXED procedure in SPSS) reveal that goal disengagement buffered people from worsening relationship satisfaction associated with poor spousal sleep [95% CI B (−1.17, −0.12)], in part via increases in actor active coping [95% CI B (−0.32, −0.02)] and decreases in partner self-blame [95% CI B (−0.28, −0.01)]. Goal reengagement was related to diminished relationship satisfaction in response to poor own sleep [95% CI B (0.59, 1.79)], in part through increases in actor behavioral disengagement [95% CI B (0.05, 0.41)]. Discussion These findings point to a need for future studies to examine goal adjustment capacities and relationship-specific coping strategies as potential targets of intervention to maintain peoples' relationship satisfaction in the face of sleep problems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".