GOAL ADJUSTMENT CAPACITIES DURING COVID-19: CONTEXT-DEPENDENT BENEFITS FOR EMOTIONAL WELL-BEING
Bibliographic record
Abstract
Abstract Increased constraints and lost opportunities inherent in the COVID-19 pandemic can threaten important life goals and erode emotional well-being. Theories of lifespan development have identified goal adjustment capacities (goal disengagement and goal reengagement) as core self-regulatory resources that can buffer against declines in well-being. However, little is known about the pandemic-related contextual circumstances under which goal adjustment capacities may become more or less beneficial for well-being. Using longitudinal data from a nationally-representative sample of Americans across the adult lifespan (aged 18-80, n=286), we examined the consequences of goal adjustment capacities for emotional well-being under circumstances when individuals reported lower or higher constraints than normal in their lives. Specifically, multilevel models tested whether the influence of between-person differences in (Level 2) goal disengagement and goal reengagement on well-being were moderated by (Level 1) within-person fluctuations in perceived constraints. Analyses controlled for age, sex, education, and income. We observed cross-level Goal Reengagement x Perceived Constraints interactions for depressive symptoms, perceived stress, and positive affect (bs = -.11 to .07, ps < .05), but not negative affect. Results showed that the benefits of goal reengagement for depressive symptoms, perceived stress, and positive affect were pronounced on occasions when participants reported lower (vs. higher) than average perceived constraints in their lives. Findings point to the moderating role of pandemic-related contextual circumstances and suggest that goal reengagement may be most beneficial when individuals have fewer constraints than usual in their lives and may thus able to capitalize on opportunities to pursue new attainable goals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".