Assessing the Relationship Between Depression and Obesity Using Structural Equation Modeling
Bibliographic record
Abstract
In this project we used structural equation modeling to analyze the data collected for the Canadian Community Health Survey (CCHS) Cycle 1.2 - Mental Health and Well-Being conducted by Statistics Canada. The data are cross-sectional. We looked at the relation between depression and obesity adjusting for gender, socioeconomic status, gene-environment interactions, eating and physical activity and stress. We used the AMOS and Mplus softwares to analyze our data. The first one used continuous variables for depression ("persistence of depression", in years) and obesity ("body mass index"-BMI), while the second used categorical variables: lifetime depression, 12 month depression and obesity (normal weight, overweight and obese). We also used two variables to measure different aspects of stress: self-perceived ability to handle an unexpected problem and work stress-social support. We fitted the models across the entire data, but also across different groups: males versus females and groups based on gender and BMI. The results indicated that the relationship between depression and obesity is different across gender. The limitations of the study are also discussed.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".