Goal disengagement, functional disability, and depressive symptoms in old age.
Bibliographic record
Abstract
OBJECTIVES: This longitudinal study examined the associations between older adults' goal adjustment capacities (i.e., goal disengagement and goal reengagement capacities), functional disability, and depressive symptoms. It was expected that goal disengagement capacities would prevent an adverse effect of heightened functional disability on increases in depressive symptoms. METHOD: Multivariate regression analyses were conducted, using four waves of data from a six-year longitudinal study of 135 community-dwelling older adults (>60 years old). RESULTS: Depressive symptoms and functionality disability increased over time. Moreover, poor goal disengagement capacities and high levels of functional disability forecasted six-year increases in depressive symptoms. Finally, goal disengagement buffered the association of functional disability with increases in depressive symptoms. No associations were found for goal reengagement capacities. CONCLUSION: The findings suggest an adaptive role for goal disengagement capacities in older adulthood. When confronted with increases in functional disability, the capacity to withdraw effort and commitment from unattainable goals can help protect older adults from experiencing long-term increases in depressive symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".