Does Money Matter? Characteristics Associated With Joint Pain Medication Access Among Older Adults
Bibliographic record
Abstract
Abstract The experience of pain is a widespread phenomenon among adults, especially older adults, and entails high costs to both individuals and society. The objective of the current research is to determine if the ability to pay and supplementary insurance are factors associated with pain medication among individuals over 50. Data came from Survey of Health, Aging and Retirement in Europe (SHARE). The sample included 64,281 individuals 50+ from nineteen European countries and Israel. Joint pain was common with one out of three reporting joint pain. Prevalence of pain was similar among different age groups, and more women reported joint pain. Among those in pain, about 21.5% of the individuals reported mild pain, 52.9% moderate and 26% severe pain. In the multivariate logistic regression, we found that men and those older than 60 suffered more from joint pain, while controlling for education and subjective assessment of the ability to cope economically (Able to make ends meet). A large percentage of those with pain were not taking medication to manage their pain, and there were significant demographic differences between those that did and did not take medication. Those that took medication were younger, male, had more education, were able to cope economically and had supplementary insurance. Our study showed that about half of the individuals with pain were not taking medication to manage their pain. Our results demonstrate that among individuals over 50 in Europe income is strongly associated with taking pain medication and that there is economic inequity in medication access.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".