Determining use of preventive health care in Ontario: comparison of rates of 3 maneuvers in administrative and survey data.
Bibliographic record
Abstract
OBJECTIVE: To examine rates of influenza vaccination, mammography, and Papanicolaou smear by comparing data obtained from the Ontario Health Insurance Plan administrative database with rates as self-reported in the Canadian Community Health Survey. DESIGN: Retrospective cohort study using data from Statistics Canada's 2000-2001 Canadian Community Health Survey and from the Ontario Health Insurance Plan administrative database for the same period. SETTING: Ontario. PARTICIPANTS: Those aged 12 and older who had received influenza vaccination, women aged 35 or older who had had mammograms within the past 2 years, and women aged 18 or older who had had Pap smears within the past 3 years who were surveyed during the Canadian Community Health Survey in 2001. MAIN OUTCOME MEASURES: Rates of influenza vaccination, mammography, and Pap smear in Ontario's 14 Local Health Integration Networks by network, age group, and socioeconomic status. RESULTS: Rates varied by health network. Analysis by age showed that influenza vaccination rates increased with age and peaked among those 75 and older. Rates of mammography screening increased with age but dropped substantially among those 75 and older. Rates of Pap smear peaked among those 20 to 39 and decreased with increasing age. Rates of mammography and Pap smear increased with rising socioeconomic status, but influenza vaccination rates did not differ substantially by socioeconomic status. Rates for all 3 preventive maneuvers were lower in the Ontario Health Insurance Plan data than in the self-reported Canadian Community Health Survey data. CONCLUSION: There are obstacles to finding out the true rates of preventive health care use in Ontario. We need to ascertain these rates in order to establish a criterion standard for delivery of these services. Development of programs to target specific geographic locations, socioeconomic classes, and high-risk groups are needed to increase the overall use of preventive health services in Ontario.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".