The impact of co-morbid mental and physical disorders on presenteeism
Bibliographic record
Abstract
OBJECTIVES: This study sought to: (i) explore the impact of mood disorders (such as depression, bipolar disorder, mania, or dysthymia) and five age-related chronic physical conditions (arthritis, back pain, diabetes, heart disease, and hypertension) on presenteeism (as indicated by self-reported activity limitations at work), and (ii) examine how mood disorders interact with each physical condition to affect this work outcome. METHODS: Using Canadian Community Health Survey (CCHS) data, we modeled the relationships between self-reported restrictions at work and each health condition. We then calculated synergy indices (SI) for the interaction between mood disorders and each of the five physical conditions. RESULTS: All six health conditions were associated with presenteeism. The strongest association was observed for back pain [prevalence ratio (PR) 2.70, 95% confidence interval (95% CI) 2.57-2.83] and the weakest for hypertension (PR 1.18, 95% CI 1.11-1.25). The unadjusted SI indicated no interactions between mood disorders and any of the physical conditions, while the adjusted SI indicated statistically significant interactions between mood disorders and each of the five physical conditions. The statistically significant adjusted interactions were in a negative direction, such that having a mood disorder concurrent with a chronic physical condition was associated with a lower burden of presenteeism than expected. Post-hoc analyses revealed that this unexpected finding was attributable to adjustment for other co-morbid health conditions, particularly arthritis and back pain. CONCLUSIONS: Our results suggest that targeting chronic physical conditions or mood disorders may be productive in reducing presenteeism. The combined effect on presenteeism when the two types of conditions occur simultaneously is similar to the additive effect of these conditions when each occurs in isolation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".