Occupational therapy return to work interventions for persons with trauma and stress-related mental health conditions: A scoping review
Bibliographic record
Abstract
BACKGROUND: Trauma and stress-related mental health conditions can impact a person's ability to participate in work and can cause disruptions in employment. Best practice guidelines for occupational therapy return to work interventions with these populations are limited. OBJECTIVE: To identify and describe occupational therapy return to work interventions for trauma and stress-related mental health conditions. METHODS: Using a scoping review methodology, research databases were searched for papers relating to occupational therapy, return to work interventions, and trauma and stress-related mental health conditions. Three reviewers independently applied selection criteria and systematically extracted information. Data were extracted and synthesized in a narrative format. RESULTS: The search produced 18 relevant papers. The interventions described were more often person-focused versus environment- and occupation-focused, and many were carried out by multidisciplinary teams, making it difficult to identify best practices for occupational therapists in this area. CONCLUSION: Emerging practices include the Swedish "ReDO" intervention, support for active military members to manage operational stress to remain at work, and multidisciplinary team treatment. Further research, including studies with direct focus on the implications of occupational therapy interventions for return to work with trauma and stress-related mental health conditions, is required.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".