Socioeconomic Gradient of Functional Limitations in Individuals Diagnosed with Mood Disorders
Bibliographic record
Abstract
The association between unfavorable socioeconomic conditions and higher prevalence of mood disorders has been well established. The detrimental impact of mood disorders on disability is also well established. Less is known about the socioeconomic gradient of disability in individuals with mood disorders. The objective of this study was to investigate whether a socioeconomic gradient in functional limitation existed in individuals with mood disorders living in Canada (4,720 women and 2,645 men). The study was based on secondary analyses of data collected in the Canadian Community Health Survey in 2005. Significant positive associations between prevalence of functional limitations and age, number of chronic conditions, and number of consultations with medical doctors were found for both genders. Adjusting for these factors, the odds of functional limitations declined with increasing socioeconomic status for both men and women. This gradient was more evident with income level than with education level. The odds of having functional limitations for women in the lowest income decile were 2.33 times the odds for women in the highest income decile. The corresponding odds for men were 3.56. Compared with post secondary graduates, women and men with less than high school education had 1.46 and 2.31 higher odds of functional limitations, respectively. No significant gender difference was observed in the associations between socioeconomic indicators and functional limitations. These findings suggest the importance of assessing functional limitations in individuals with mood disorders, especially those living in disadvantaged economic conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".