Cognitive work hardening: A return-to-work intervention for people with depression
Bibliographic record
Abstract
Mental health claims in the workplace are rising, particularly those due to depression. Associated with this is an increase in disability costs for the employer and the disability insurer, but even more important is the human suffering that results. While treatments are available for the depression there is a gap in interventions that specifically target return-to-work preparation. This paper presents cognitive work hardening, a treatment intervention that can bridge this gap by addressing the unique functional issues inherent in depression with a view to increasing return-to-work success. Cognitive work hardening applies the proven principles of classical work hardening (which has typically been applied to people with physical injuries) to the mental health domain. This paper explains how the occupational therapy principle of occupation and the core competency, enablement, are utilized and applied in cognitive work hardening. Key skills of the occupational therapist are also discussed. In addition, the paper considers the relationship of cognitive work hardening to recovery and mental illness, and the role it plays among workplace-based return-to-work interventions in the current movement toward non-clinical return-to-work interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".