Mood and anxiety disorders, the association with presenteeism in employed members of a general population sample
Bibliographic record
Abstract
Summary Aims– The term “presenteeism” is used to describe workers who are present in the workforce, but who are not functioning at full capacity. The objective of the study was to describe the impact of mood and anxiety disorders on presenteeism in a population sample.Methods– Random digit dialing was used to select a sample of n= 3345 subjects between the ages of 18 and 64. A computer assisted telephone interview that included theMini Neuropsychiatric Diagnostic Interview(MINI), theStanford Presenteeism Scale6 (SPS-6) and a pharmacoepidemiology module was administered.Results– Among subjects with comorbid mood and anxiety disorders 75.0% reported interference with their work compared with only 13.3% of subjects without mood or anxiety disorders. Mood and anxiety disorders were associated with lower presenteeism ratings. Regression analysis uncovered a significant gender by anxiety disorder interaction, indicating that the effect of anxiety disorders was greater in men than women.Conclusions– This is the first study to report the impact of mental disorders on presenteeism in a general population sample. The results confirm that the problem of presenteeism is not restricted to specific occupational groups, but is instead a widespread problem in the general population. Declaration of Interest: This study was funded by the Alberta Depression Initiative through the Institute of Health Economics (www.ihe.ab.ca). Dr. Esposito was supported by an International Resident Fellowship from the University of Calgary. Dr. Patten is a Health Scholar with the Alberta Heritage Foundation for Medical Research and a Fellow with the Institute of Health Economics. Dr. Wang is a New Investigator with the Canadian Institutes for Health Research.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".