Obesity Research in Canada: Literature Overview of the Last 3 Decades
Bibliographic record
Abstract
OBJECTIVE: To identify research published on obesity in Canada, to explore the range of areas studied, and to identify gaps and areas that merit future research attention. RESEARCH METHODS AND PROCEDURES: Medline and International Pharmaceutical Abstracts databases were searched from 1970 onwards. Original articles were identified and categorized by areas of interest. RESULTS: A total of 1186 relevant articles were identified: 17, 136, 687, and 346 articles during the 1970s, 1980s, 1990s, and 2000 to 2003, respectively. Of the articles, 816 were considered original studies and accepted for this analysis. Twelve research areas were identified: basic science involving animal experiments (29%), human experiments (16%), populations surveys (14%), obesity-related comorbidities (13%), diagnostic/surgical issues (11%), nonpharmacological approaches (7%), drug-related issues (4%), anthropometrics (2%), impact of weight loss (2%), cost/healthcare use (1%), attitudes/perceptions (0.9%), and models/procedures (0.5%). Two-thirds of all research was conducted in Quebec (34%) and Ontario (33%). DISCUSSION: Given the multifactorial nature of obesity, Canadian obesity research covers a broad range of areas with a predominance of basic science but lesser emphasis on community and primary care studies. Furthermore, there was a paucity of research on either clinical management of medical conditions in obese patients or clinical aspects that go beyond weight loss. Thus, although Canada appears well represented in basic research, more attention to exploration of clinical issues and healthcare delivery for obese patients appears warranted.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.057 | 0.132 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".