Sociodemographic inequality in joint‐pain medication use among community‐dwelling older adults in Israel
Bibliographic record
Abstract
Joint pain is a common experience among adults aged 65 and over. Although pain management is multifaceted, medication is essential in it. The paper examines the use of medication among older adults with joint pain in Israel and asks whether socioeconomic factors are associated with this usage. The data, harvested, from the Survey of Health, Aging and Retirement in Europe (SHARE), include 1,294 randomly selected community-dwelling individuals aged 65 and over in Israel. Bivariate analysis and logistic regression are used to identify factors associated with the presence of joint pain medication use. About 38% of respondents report experiencing joint pain and 45% of those who so report are not taking prescription medication. Back pain is the most common location, reported by 64% of individuals who report joint pain. Taking medication is independently associated with younger age (OR = 0.965, 95% CI = 0.939-0.991), more education (OR = 1.044, 95% CI = 0.998-1.091), and better ability to cope economically (OR = 1.964, 95% CI = 1.314-2.936). However, older age and ability to cope economically are independently associated with women (OR = 0.964, 95% CI = 0.932-0.998 and OR = 2.438, 95% CI = 1.474-4.032, respectively) but not with men. It is suggested that socioeconomic inequality exists in healthcare access among adults aged 65 and over. Since income and gender are strongly associated with taking pain medication, physicians should follow-up on women and less affluent people to ensure that medication prescribed has been obtained. Policymakers should consider programs that would facilitate better access to pain medication among vulnerable older individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".