Incidence rates of sickness absence related to mental disorders: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: Over the past decade, growing attention has been given to the mental health of workers. One way to examine the mental health of workers is to look at the incidence rates of mental illness-related sickness absence. There is a scarcity of literature in which the incidence rates of mental illness-related sickness absence among different countries have been considered together. The purpose of this systematic literature review is to address the question: Are there similarities and differences in the incidence rates of mental disorder-related sickness absence among and within OECD identified Social Democratic, Liberal and Latin American country categories? In this paper, we seek to identify differences and similarities in the literature rather than to explain them. With this review, we lay the groundwork for and point to areas for future research as well as to raise questions regarding reasons for the differences and similarities. METHODS: A systematic literature search of the following databases were performed: Medline Current, Medline In-process, PsycINFO, Econlit and Web of Science. The search period covered 2002-2013. The systematic literature search focused on working adults between 18-65 years old who had not retired and who had mental and/or substance abuse disorders. Intervention studies were excluded. The search focused on medically certified sickness absences. RESULTS: A total of 3,818 unique citations were identified. Of these, 10 studies met the inclusion/exclusion criteria; six were from Social Democratic countries. Their quality ranged from good to excellent. There was variation in the incidence rates reported by the studies from the Social Democratic, Liberal and Latin American countries in this review. CONCLUSIONS: The results of this systematic review suggest that this is an emerging area of inquiry that needs to continue to grow. Priority areas to support growth include cross jurisdictional collaboration and development of a typology characterizing the benefit generosity and work integration policies of sickness absence schemes. Finally, the literature should be updated to reflect changes in sickness absence benefit schemes over time.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".