Where Does It Hurt? A 4-S Model of Pain Onset and Recovery Across Body Sites Among Older Adults
Bibliographic record
Abstract
OBJECTIVES: Pain is an increasingly widespread problem for older adults, contributing to higher health care costs and lower quality of life. Though the prevalence of pain is well documented in national studies, less is known about where pain occurs in the body, and even less about the dynamic processes of onset and recovery. We examine the bodily distribution of pain and its covariates and propose a framework to systematize how pain sites remain stable, spread, switch, or subside over time. METHODS: Analyses use longitudinal data from the National Social Life, Health, and Aging Project (n = 2,600), a survey featuring pain site measures usually available only in clinical samples. Descriptive statistics and supplementary regression models showcase pain dynamics across bodily locations and over time. RESULTS: Pain is most prevalent in the legs and low back. During the observed period, 57% of respondents had at least 1 onset location and 79% experienced at least 1 recovery location. Many displayed complex patterns of onset and recovery in tandem. Gender, exposure to childhood trauma, and perceived neighborhood danger emerged as key correlates of pain prevalence across bodily locations and pain dynamics over time, shaping whether pain remained stable, spread, switched locations, or subsided. DISCUSSION: Older Americans with pain rarely have their discomfort confined to 1 location, and many experience concurrent onset and recovery over time. We propose the 4-S model as a framework to understand the complex interplay of pain onset and recovery and offer ways to integrate this framework into ongoing investigations of late-life pain.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".