Bibliographic record
Abstract
Abstract Chronic pain, highly prevalent among older adults, is associated with worse health. However, little is known about its impact on successful aging (SA). SA is a multidimensional concept encompassing not only physical and psychological health, but also social connectedness and functional independence in later life. This study examines the impact of chronic pain on SA over time and identifies demographic and socioeconomic factors that may buffer or exacerbate their association. Data are from the Canadian Longitudinal Study on Aging (CLSA), a 3-wave panel study including 30,097 adults aged 45 and older. The primary outcome is SA, which we constructed from 6 domains with indicators collected identically at all waves. We estimate mixed-effects models of SA as a function of pain severity (no pain, mild/moderate pain, severe pain). Models control for age, gender, marital status, race/ethnicity, immigrant status, education, and income, and include appropriate interactions to test buffering. Initial results indicate that chronic pain is associated with lower SA scores, with more severe pain linked to lower baseline scores and greater declines over time. Among covariates, income is particularly salient as a moderator of the association. These findings highlight that chronic pain undermines multiple dimensions of wellbeing essential to successful aging, extending beyond physical and mental health. The significant role of income highlights socioeconomic disparities in pain experiences, suggesting that addressing these inequities could enhance successful aging trajectories.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".