<i>Don’t Change Much</i>
Bibliographic record
Abstract
Few studies have assessed differences in the prevalence of and economic burden attributable to tobacco smoking, excess weight, physical inactivity, and alcohol use by gender. This article examines these gender differences in Canadians between the ages of 30 and 64 years. It also estimates the potential cost avoidance if the prevalence of the four risk factors (RFs) were reduced modestly in males. Data on the prevalence of the RFs and the relative risk of disease associated with each of the RFs were combined to calculate population-attributable fractions. A prevalence-based cost-of-illness approach was used to estimate the economic burden associated with the four RFs. Middle-aged Canadian males are more likely to smoke tobacco (26.4% vs. 20.2%), consume hazardous or harmful levels of alcohol (14.6% vs. 8.2%), and have excess weight (65.6% vs. 47.1%) than middle-aged Canadian females, resulting in an annual economic burden that is 27% higher in males than females. No significant differences were observed in the proportion of males who are physically inactive (48.4% vs. 49.4%). Modelling only a 1% annual relative reduction each year through to 2036 would result in a cumulative cost avoidance between 2013 and 2036 of $50.7 billion. The differences in RF prevalence between middle-aged males and females have an important effect on the population's economic burden. A modest annual reduction in the four RFs in males can significantly affect population health and the economy over time.
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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 | 0.020 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.020 |
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".