Class inequalities in prescription drug use, the case of hormone replacement therapy
Bibliographic record
Abstract
Despite extensive research examining the relationship between social class and health, little is known about the role of social class in prescription drug use. This study examines social class inequalities in prescription drug use with a specific focus on hormone replacement therapy (HRT) use among older women. Secondary analysis was conducted using data from the 1996-97 National Population Health Survey (NPHS) (n = 8759) (Statistics Canada, 1996-97). Crosstabs and a logistic regression analysis were utilized in order to determine the sociodemographic and health characteristics of women who use HRT as well as differences in use in terms of social class while controlling for sociodemographic and health characteristics. The results of the analysis indicated that just under one quarter (24.3%) of women between 45 to 64 years of age reported HRT use in the past month. Results of the bivariate analysis indicated that certain sociodemographic and health characteristics were found to be associated with HRT use, however, most were found to have only weak associations. Results of the multivariate analysis demonstrated that when examining social class and controlling for other va iables, income was found to have the strongest association with HRT use while education and labour force status were not significant. This study provides baseline prevalence rates of HRT use in Canada. Insights into the relationship between social class and users and nonusers of HRT are developed using the political economy of aging framework and directions for future research topics are presented.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".