Self-perceived health status among adults with obesity in Quebec: a cluster analysis
Bibliographic record
Abstract
PURPOSE: People with obesity are a highly heterogeneous group. Characterizing this heterogeneity may help to improve public health by offering adapted interventions and treatments to more homogeneous sub-groups among obese patients. This research aims to (1) identify distinct clusters of people with obesity based on demographic, behavioural, and clinical factors in the province of Quebec (Canada) and (2) assess the association of these clusters with selfperceived health. METHODS: from the 2015-2016 Canadian Community Health Survey in Quebec. Clusters were based on demographic, clinical, and behavioural characteristics. The clusters were tested for association with poor selfperceived health using logistic regression. RESULTS: Three clusters of individuals with obesity were identified. These were (1) young individuals, (2) people with higher levels of depression and anxiety, and (3) older adults with high comorbidity. Those with high levels of depression and anxiety (9% of men vs. 13% of women) were associated with the poorest selfperceived health. CONCLUSIONS: People with obesity in Quebec can be categorized into three clusters based on demographic, clinical, and behavioural characteristics. The findings of this study draw attention to the need to examine the coexistence of obesity with depression and anxiety, particularly as it relates to selfperceived health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".