Goal adjustment capacities, coping, and subjective well-being: The sample case of caregiving for a family member with mental illness.
Bibliographic record
Abstract
This study examined the associations between goal adjustment capacities, coping, and indicators of subjective well-being in 2 waves of data from individuals who provide care for a family member with mental illness. We hypothesized that goal adjustment capacities would predict higher levels of subjective well-being by facilitating coping with caregiving stress. Results showed that goal disengagement was associated with effective care-specific coping (e.g., less self-blame and substance use). Goal reengagement was also associated with effective care-specific coping (e.g., positive reframing), but at the same time it predicted the use of less effective strategies (e.g., venting and self-distraction). Moreover, goal disengagement predicted lower levels of caregiver burden and depressive symptoms and buffered the longitudinal effect of caregiver burden on increases in depressive symptoms. Goal reengagement, by contrast, predicted higher levels of caregiver burden and purpose in life and buffered the cross-sectional association between caregiver burden and depressive symptoms. Finally, effective (and less useful) care-specific coping statistically explained the adaptive (and maladaptive) effects of goal adjustment capacities on participants' well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".