Higher Perceived Stress Scale Scores Are Associated with Higher Pain Intensity and Pain Interference Levels in Older Adults
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of bodily pain measures (pain intensity and interference) in elderly people and their relationship with Perceived Stress Scale (PSS) scores. DESIGN: Cross-sectional. SETTING: Community. PARTICIPANTS: A representative community sample of 578 individuals aged 70 and older (mean age 78.8, 63% female). MEASUREMENTS: The prevalence of pain intensity and pain interference and their relationship with PSS scores, demographic factors, past medical history, and neuropsychological testing scores were examined. Pain intensity and pain interference were measured using the Medical Outcomes Study 36-item Short-Form Survey bodily pain questions. RESULTS: Bivariate analysis for pain measures showed that PSS scores, neuropsychological test scores, and medical histories were associated with pain intensity and interference. Logistic regression showed that higher PSS scores were significantly associated with greater odds of having moderate to severe pain intensity and moderate to severe pain interference (with and without the inclusion of pain intensity in the models). CONCLUSION: Higher PSS scores are associated with greater pain intensity and interference. In this cross-sectional analysis, directionality cannot be determined. Because perceived stress and pain are potentially modifiable risk factors for cognitive decline and other poor health outcomes, future research should address temporality and the benefits of treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".