Self‐management strategies in overweight and obese Canadians with arthritis
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of overweight and obese Canadians with arthritis and to describe their use of arthritis self-management strategies, as well as explore the factors associated with not engaging in any self-management strategies. METHODS: Respondents to the 2009 Survey on Living with Chronic Diseases in Canada, a nationally representative sample of 4,565 Canadians age ≥20 years reporting health professional-diagnosed arthritis (including more than 100 rheumatic diseases and conditions), were asked about the impact of their arthritis and how it was managed. Among the overweight (body mass index [BMI] 25-29.9 kg/m(2)) and obese (BMI ≥30 kg/m(2)) individuals with arthritis (n = 2,869), the use of arthritis self-management strategies (i.e., exercise, weight control/loss, classes, and community-based programs) were analyzed. Log binomial regression analyses were used to examine factors associated with engaging in none versus any (≥1) of the 4 strategies. RESULTS: More than one-quarter (27.4%) of Canadians with arthritis were obese and an additional 39.9% were overweight. The overweight and obese individuals with arthritis were mostly female (59.5%), age ≥45 years (89.7%), and reported postsecondary education (69.0%). While most reported engagement in at least 1 self-management strategy (84.9%), less than half (45.6%) engaged in both weight control/loss and exercise. Factors independently associated with not engaging in any self-management strategies included lower education, not taking medications for arthritis, and no clinical recommendations from a health professional. CONCLUSION: Fewer than half of the overweight and obese Canadians with arthritis engaged in both weight control/loss and exercise. The provision of targeted clinical recommendations (particularly low in individuals that did not engage in any self-management strategies) may help to facilitate participation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".