Gender differences in the utilization of health care services.
Bibliographic record
Abstract
BACKGROUND: Studies have shown that women use more health care services than men. We used important independent variables, such as patient sociodemographics and health status, to investigate gender differences in the use and costs of these services. METHODS: New adult patients (N = 509) were randomly assigned to primary care physicians at a university medical center. Their use of health care services and associated charges were monitored for 1 year of care. Self-reported health status was measured using the Medical Outcomes Study Short Form-36 (SF-36). We controlled for health status, sociodemographic information, and primary care physician specialty in the statistical analyses. RESULTS: Women had significantly lower self-reported health status and lower mean education and income than men. Women had a significantly higher mean number of visits to their primary care clinic and diagnostic services than men. Mean charges for primary care, specialty care, emergency treatment, diagnostic services, and annual total charges were all significantly higher for women than men; however, there were no differences for mean hospitalizations or hospital charges. After controlling for health status, sociodemographics, and clinic assignment, women still had higher medical charges for all categories of charges except hospitalizations. CONCLUSIONS: Women have higher medical care service utilization and higher associated charges than men. Although the appropriateness of these differences was not determined, these findings have implications for health care.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.007 | 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".