Strategies used by Occupational Therapists to overcome return-to-work obstacles perceived by workers with common mental disorders
Bibliographic record
Abstract
Introduction: Few studies have focused on identifying distinctive strategies implemented for overcoming return-to-work (RTW) barriers perceived by people with common mental disorders (CMDs), and their impact on RTW. The study aimed to document the strategies used by occupational therapists to overcome RTW obstacles identified by people with CMDs, and to explore the impact of these strategies on employees' self-efficacy. Method: Ten workers followed by three occupational therapists were recruited for this study. Based on the participants' ROSES scores, the occupational therapists identified in a logbook the dimensions to be worked on and the strategies they implemented for each dimension. Data was analyzed with the use of thematic analysis and descriptive statistics. Results: Three dimensions of the ROSES were most frequently targeted by the occupational therapists: job demands, fear of relapse and difficult relation with the immediate supervisor. The main strategies used to overcome these obstacles were work-oriented and Cognitive Behavioral Therapy-based interventions. Most of the participants have increased their self-efficacy for RTW after using these strategies. Eighty percent of the participants returned to work at the end of the study. Conclusion: The use of work-oriented and CBT-based interventions by occupational therapists appears to be useful in improving participants' self-efficacy and promoting their return to work.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".