The Role of Depression and Chronic Pain Conditions in Absenteeism: Results From a National Epidemiologic Survey
Bibliographic record
Abstract
OBJECTIVE: This study examined whether depression is associated with absenteeism in a sample of individuals with chronic pain. METHODS: Data were obtained from the Canadian Community Health Survey Cycle 1.2. Key variables were chronic pain, defined as fibromyalgia, arthritis/rheumatism, back problems, and migraine headaches, absenteeism, and depression. The sample comprised 9,238,154 individuals who reported at least one chronic pain condition and were absent from their job in the previous week because of illness or disability. RESULTS: Nineteen percent of absent individuals met criteria for major depression versus 7.9% of non-absent individuals. The presence of major depression represented a three-fold risk of absenteeism. Other risk factors for absenteeism included younger age, higher income, and more education. CONCLUSIONS: Comorbid depression and chronic pain represents a significant source of disability in the workforce.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".