Bibliographic record
Abstract
Background: No studies have examined the frequency of thyroid screening in the Canadian population, and whether thyroid screening and medication use vary by sex, race, income, and preexisting health conditions. Methods: Using data from the 2011, 2012 cycles of the Canadian Community Health Survey, we report rates of thyroid screening among Quebec residents ≥35 (n=7024) and rates of thyroid medication use among Quebec residents ≥35 (n=16,081). We examine variations in medication use and screening by sex, age, race, immigration status, access to a regular doctor, and health conditions that have been linked to thyroid disease. Results: Of the Quebec residents ≥35, 10.3% reported taking thyroid medication and 0.4% reported that the last blood test a physician ordered was to check for a new thyroid condition. Canadian-born residents and those who identified as White reported higher medication use and screening rates, compared to immigrants and those who identified as visible minorities. Racial disparities were especially pronounced, with White Quebec residents reporting three times greater odds of thyroid screening than visible minorities. The strongest predictors of both thyroid medication use and screening were access to a regular doctor. Despite women being eight times more likely to suffer from thyroid disease, women were not significantly more likely to be screened, compared to men (odds ratio=1.38, 95% confidence interval: 0.74–2.60). Discussion: Strategies are needed to decrease disparities in thyroid screening and medication use. Interventions that target health systems (e.g., increasing physician supply), providers (continuing professional education modules about thyroid disease for family physicians), and recipients of care (multilanguage public awareness campaigns and posters at walk-in clinics that describe common symptoms of different thyroid disorders) should be implemented and tested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".