Pain and Depression in Late Life: Mastery as Mediator and Moderator
Bibliographic record
Abstract
OBJECTIVES: This study examines how mastery mediates and moderates the relationship between pain and depression among older adults, as well as the extent to which these processes differ by the timing of pain in late life, while utilizing statistical methods that comprehensively control for time-stable confounds. METHODS: Data are derived from multiple observations of adults aged 65 years and older in the Washington, DC, metropolitan area over a 4-year period. Fixed effects models are used to control for time-stable influences. RESULTS: With all time-stable influences controlled, pain is positively related to symptoms of depression, although this relationship is substantially reduced in comparison with a model in which all time-stable confounds are not held constant. Mastery does not mediate this relationship because pain is not significantly related to mastery once time-stable factors are taken into account. Mastery buffers the relationship between pain and depression, but only for elders later in late life. DISCUSSION: This study suggests that a synthesis of stress process and life course perspectives is critical for understanding how pain influences depression in late life. However, research that does not comprehensively control for time-stable factors may overestimate the consequences of pain for older adults.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".