Racial differences in social network experience and perceptions of benefit of arthritis treatments among New York City Medicare beneficiaries with self‐reported hip and knee pain
Bibliographic record
Abstract
OBJECTIVE: To determine whether social network experience and perceptions of benefit of arthritis treatments influence the decision to seek diagnosis and treatment. METHODS: A population-based telephone survey of 515 black and 455 white Medicare beneficiaries was conducted. Validated questionnaires adapted for use in a telephone interview were used to identify people with self-reported symptoms of hip or knee pain. Treatment history for arthritis-related pain and perceptions of benefit of treatment were also assessed. RESULTS: Forty-two percent of blacks and 31% of whites reported hip or knee pain. Forty-two percent of blacks and 65% of whites reported knowing someone who had surgery for hip or knee pain (P < 0.0001). Blacks were less likely than whites to report that surgery had helped someone they knew with hip or knee pain (not significant). CONCLUSION: Blacks know fewer people who have had surgical treatment of hip and knee pain than whites and appear to be less likely to perceive that such treatment is beneficial.
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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) | 0.003 | 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".