Bibliographic record
Abstract
OBJECTIVE: To provide detailed demographic profiles of prescription drug utilization and expenditures in order to isolate the impact of demographic change from other factors that affect drug expenditure trends. DATA SOURCES/STUDY SETTING: Demographic information and drug utilization data were extracted for virtually the entire British Columbia (BC) population of 1996 and 2002. All residents had public medical and hospital insurance; however their drug coverage resembled the mix of private and public insurance in the United States. STUDY DESIGN: A series of research variables were constructed to illustrate profiles of drug expenditures and drug utilization across 96 age/sex strata. DATA COLLECTION/EXTRACTION METHODS: Drug use and expenditure information was extracted from the BC PharmaNet, a computer network connecting all pharmacies in the province. PRINCIPAL FINDINGS: Per capita drug expenditures increased at an average annual rate of 10.8 percent between 1996 and 2002. Population aging explained 1.0 points of this annual rate of expenditure growth; the balance was attributable to rising age/sex-specific drug expenditures. CONCLUSIONS: Relatively little of the observed increase in drug expenditures in BC could be attributed to demographic change. Most of the expenditure increase stemmed from the age/sex-specific quantity and type of drugs purchased. The sustainability of drug spending therefore depends not on outside forces but on decisions made by policy makers, prescribers, and patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".