Partner Contributions to Goal Pursuit: Findings From Repeated Daily Life Assessments With Older Couples
Bibliographic record
Abstract
OBJECTIVES: This study focuses on the role of spouses for facilitating goal progress during a phase in life when individual resources for goal pursuit are particularly limited. Specifically, we examined the moderating role of relationship characteristics in old age for time-varying partner involvement-goal progress associations as couples engaged in their everyday lives. We also assessed time-varying associations between everyday goal progress, effectiveness of partner contributions, and spousal satisfaction with this contribution. METHODS: We used multilevel modeling to analyze data from 118 couples (Mage = 70 years, SD = 5.9; 60-87 years, 50% women; 57% White). Both partners reported their personal goals and provided information on relationship satisfaction, conflict, and support. They also provided simultaneous ratings of everyday goal progress, effort, partner involvement as well as effectiveness of and satisfaction with partner contribution up to three times daily over 7 days. RESULTS: In line with expectations, higher relationship satisfaction and support and lower conflict were associated with higher goal progress when the partner was involved in goal pursuit. Both effectiveness of and satisfaction with partner contributions were positively associated with everyday goal progress. DISCUSSION: Whether partner involvement is beneficial for goal progress depends on characteristics of the relationship as well as what partners actually do in everyday life. This highlights the importance of considering both stable person characteristics as well as time-varying processes to capture the complexity of goal pursuit in older couples.
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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 | 0.004 | 0.011 |
| 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 | 0.002 |
| Research integrity | 0.001 | 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".