Trends in physical activity research in Canada
Bibliographic record
Abstract
The purpose of this study was to assess Canada's physical activity research productivity by (i) assessing Canada's contribution to global physical activity research, (ii) determining trends in Canadian physical activity research over time, and (iii) identifying research areas of greater or lesser representation. A Medline search was performed to identify physical activity research published between 1990 and 2005 from Canadian institutions or using Canadian data. Trends over time were plotted and compared with global physical activity research patterns. Original and review articles were sorted based on subject characteristics, nature of physical activity contribution, province and institution of origin, and research area or "pillar". A total of 5302 Canadian physical activity articles were identified for the period 1990-2005, representing 4.9% of global physical activity research. After manual sorting of abstracts, 3829 relevant articles were included for further analysis. A majority of Canada's physical activity research has come from Ontario (49%), followed by Quebec (21%), Alberta (10%), and British Columbia (10%). Where physical activity was the primary research focus, the biomedical and clinical pillars each accounted for 39% of Canadian research, with lesser contributions from population health (14%) and health services (2%). Physical activity research productivity in Canada has paralleled global trends over the last 15 years. There is currently less physical activity research being conducted in population health and health services than in the biomedical and clinical areas; however, these areas play an important role in the development of public health policy and programs targeted at reducing the burden of physical inactivity and 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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.045 | 0.114 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".