Goal adjustment capacities and quality of life: A meta‐analytic review
Bibliographic record
Abstract
OBJECTIVES: This meta-analysis quantified associations between goal disengagement and goal reengagement capacities with individuals' quality of life (i.e., well-being and health). METHODS: Effect sizes (Fisher's Z'; N = 421) from 31 samples were coded on several characteristics (e.g., goal adjustment capacity, quality of life type/subtype, age, and depression risk status) and analyzed using meta-analytic random effects models. RESULTS: Goal disengagement (r = 0.08, p < 0.01) and goal reengagement (r = 0.19, p < 0.01) were associated with greater quality of life. While goal disengagement more strongly predicted negative (r = -0.12, p < 0.01) versus positive (r = 0.02, p = 0.37) indicators of well-being, goal reengagement was similarly associated with both (positive: r = 0.24, p < 0.01; negative: r = -0.17, p < 0.01). Finally, the association between goal disengagement and lower depressive symptoms (r = -0.11, p < 0.01) was reversed in samples at-risk for depression (r = 0.08, p = 0.01), and goal disengagement more strongly predicted quality of life in older samples (B = 0.003, p < 0.01). CONCLUSIONS: These findings support theory on the self-regulatory functions of individuals' capacities to adjust to unattainable goals, document their distinct benefits, and identify key moderating factors.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".