Costs of and Reasons for Obesity
Bibliographic record
Abstract
The purpose of this literature review was to identify and describe the cost of obesity, the contributing factors, and the use of taxation as a possible method of control of this epidemic in a Canadian setting. A review of the current literature found on the PubMed/MEDLINE services of the National Institutes of Health as well as an analysis of Web content was conducted. The PubMed/MEDLINE search identified 677 articles pertaining to Canada and obesity, 323 articles relating to price policy, 26 articles concerning obesity and taxes, and 29 articles about obesity, Canada, and cost (1964-March 2007). The cost of obesity in Canada has been estimated at $4.3 billion per year, although no yearly figures are available. The contributing factors of obesity in Canada are multivariate, ranging from dietary patterns and physical inactivity to the availability of high-calorie foods at a low cost and greater accessibility. No Canadian studies have been conducted on the use of taxes to curb obesity or evaluating food price elasticity. The available literature suggests that the use of taxes is a viable option to address the issue of obesity in Canada.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".