Randomised trials on return-to-work programmes for major depressive disorder
Bibliographic record
Abstract
In many industrialised countries, the high prevalence and/or increasing trends in disability and work loss due to depressive disorders are worrying. For example, in Finland almost one in every three new work disability benefit recipients is disabled for work because of mental health problems.1 A common disorder such as depression, with a lifetime prevalence of up to 25% for women and 12% for men,2 would justify primary prevention programmes. Although such programmes have proven helpful for some conditions such as cardiovascular diseases, evidence for primary prevention of depression is limited, although many of the risk factors for depression have been identified as modifiable.3 While this lack of evidence is not a sufficient reason to abandon well-designed primary prevention programmes, it does provide health professionals and authorities with an incentive to focus preventive efforts on disability due to major depressive disorder through secondary prevention strategies. In recent years, there has been considerable interest in the evaluation of the potential benefits from secondary prevention of work disability. Since the 1990s, many innovative rehabilitation programmes for sick-listed employees have been developed. The so-called Sherbrooke model, which aims at an early return to work (RTW) through integration of the workplace in the treatment programme, was first applied to low back pain.4 Evidence now indicates that this type of workplace-based intervention is more effective than usual healthcare interventions for reducing sick leave and preventing work disability among employees with musculoskeletal disorders.5 The …
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".