Work outcomes of sickness absence related to mental disorders: a systematic literature review
Bibliographic record
Abstract
OBJECTIVES: The purpose of this systematic literature review is to examine the current state of knowledge regarding the return-to-work outcomes of sickness absences related to mental disorders that increase costs borne by employers. We address two questions: (1) Based on the existing literature, from the employer's perspective, what are the relevant economic return-to-work outcomes for sickness absences related to mental disorders? and (2) From the employer's economic perspective, are there gaps in knowledge about the relevant return-to-work outcomes for sickness absences related to mental disorders? SETTING: The included studies used administrative data from either an employer, insurer or occupational healthcare provider. PARTICIPANTS: Studies included working adults between 18 and 65 years old who had a sickness absence related to a mental disorder. PRIMARY AND SECONDARY OUTCOME MEASURES: The studies considered two general return-to-work outcome categories: (1) outcomes focusing on return-to-work and (2) outcomes focusing on sickness absence recurrence. RESULTS: A total of 3820 unique citations were identified. Of these, 10 studies were identified whose quality ranged from good to excellent. Half of the identified studies came from one country. The studies considered two characteristics of sickness absence: (1) whether and how long it took for a worker to return-to-work and (2) sickness absence recurrence. None of the studies examined return-to-work outcomes related to work reintegration. CONCLUSIONS: The existing literature suggests that along with the incidence of sickness absence related to mental disorders, the length of sickness absence episodes and sickness absence recurrence (ie, number and time between) should be areas of concern. However, there also seems to be gaps in the literature regarding the work reintegration process and its associated costs.
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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 | 0.009 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".