More absence, but less impact on business performance. What can we learn from Swedish approaches to managing workplace mental health?
Bibliographic record
Abstract
Using employer-level survey data, this report compares how firms in England, Ireland and Sweden are responding to the challenges of workplace mental health. The three countries adopt very different approaches to the funding and provision of healthcare services and sickness benefits, with expenditure on mental health issues much higher in Sweden than in England and Ireland. Descriptive analysis of the survey data reveals significant differences between employers in the three countries, with Swedish firms reporting higher levels of mental health-related absence and much more long-term absence. Given that overall levels of mental health issues in the three countries are similar, this suggests underreporting of mental health issues by English and Irish employers, potentially driven by cultural factors and stigma associated with mental health issues. Swedish firms also report fewer firm-level impacts of mental health absence, as well as more widespread uptake of strategic and wellbeing initiatives for mental health. In the broader context of the availability of long-term government-funded sickness pay, this suggests that the more holistic approach to managing workplace mental health issues prevalent in Sweden may lead to lower levels of detrimental performance impacts. Policy implications are discussed.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".