The Medicalization of Menopause: Implications for Recruitment of Study Participants
Bibliographic record
Abstract
OBJECTIVE: Early studies of menopause recruited sample populations from clinical settings; however, in the 1970s, populations drawn from health care settings were characterized as nonrepresentative because of symptom overreporting. This pilot study was carried out to test whether this characterization still holds true: that women who are drawn from clinical settings report more symptoms compared with women who are recruited from community and work sites. DESIGN: Open-ended interviews were carried out with patients aged 40-60 years in a physician's office (n = 50), in a chiropractor's office (n = 24), at two Breast Health Project sites (n = 50), and in several non-health care sites in the community (n = 81). Interviews were supplemented by anthropometrics and standardized return-by-mail questionnaires. RESULTS: Women who experienced hot flashes and sweating were more likely to report having spoken with a physician about menopause. However, women who were drawn from the clinical setting were not significantly more likely to describe hot flashes, sweats, or mood changes and were significantly less likely to report headaches in relation to menopause compared with a community sample. Women who were drawn from the physician's office were more likely to use hormone replacement therapy and to have had a hysterectomy. CONCLUSIONS: This study suggests that because of the medicalization of menopause, we need to rethink our assumptions about the characteristics of populations drawn from health care settings. In western Massachusetts, place of recruitment did not predict symptom frequency.
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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 | 0.313 | 0.525 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".