Exercise/Physical Activity and Weight Management Efforts in Canadians With Self‐Reported Arthritis
Bibliographic record
Abstract
OBJECTIVE: To describe the exercise/physical activity and weight management efforts of Canadians with self-reported arthritis, to examine factors associated with their engagement in these strategies to help manage their arthritis, and to explore reasons for lack of engagement. METHODS: Data were from the arthritis component of the 2009 Survey on Living with Chronic Diseases in Canada. The responses (78% response rate; n = 4,565) were weighted to be representative of Canadians (ages ≥20 years) with arthritis. Logistic regression analyses were used to examine factors associated with engaging in exercise/physical activity and weight control/loss (among overweight/obese respondents) for arthritis management purposes. RESULTS: Individuals with arthritis were mostly women (63%), ages ≥45 years (89%), overweight/obese (67%), married (68%), and white (87%), with postsecondary education (69%). Sixty-three percent were exercising and of those who were overweight or obese, 68% were trying to control/lose weight; only 46% were engaged in both. Having received a clinical recommendation was the factor most strongly associated with engaging in exercise/physical activity and/or controlling/losing weight. The most common reason for not exercising was a coexisting health condition/problem (22%), while the most common reason for not controlling/losing weight among those who were overweight/obese was that it was felt not to be necessary (51%). CONCLUSION: The provision of clinical recommendations from a health professional, providing advice on safe and suitable exercises/physical activities, as well as addressing misperceptions of the need to lose weight among the overweight/obese, may facilitate engagement in these health behaviors and ultimately reduce the consequences of arthritis.
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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 | 0.003 |
| 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".