Patient perceptions about obesity management in the context of concomitant care for other chronic diseases
Bibliographic record
Abstract
Background: Approximately 15% of Canadian adults live with two or more chronic diseases, many of which are obesity related. The degree to which Canadian obesity treatment guidelines are integrated into chronic disease management is unknown. Methods: ; 2) medical diagnosis of obesity; 3) undergone medically supervised treatment for obesity; or 4) a belief that excess/abnormal adipose tissue impairs their health. Participants must have been diagnosed with at least one of 12 prevalent obesity-related chronic diseases. Data analysis consisted of descriptive statistics. Results: One in four (26.4%) reported a diagnosis of obesity, but only 9.2% said they had received medically supervised obesity treatment. The majority (55%) agreed obesity makes managing their other chronic diseases challenging; 39% agreed their chronic disease(s) have progressed or gotten worse because of their obesity. While over half (54%) reported being aware that obesity is classified as a chronic disease, 78% responded obesity was their responsibility to manage on their own. Only 33% of respondents responded they have had success with obesity treatment. Interpretation: While awareness of obesity as a chronic disease is increasing, obesity care within the context of a wider chronic disease management model is suboptimal. More work remains to be done to make Canadian obesity guidelines standard for obesity care.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".