P-605 SUPPORTING EMPLOYERS IN IMPLEMENTING GRADUAL RETURNS TO WORK: A TRANSDIAGNOSTIC AND INCLUSIVE TOOL
Bibliographic record
Abstract
Abstract Introduction The Tool for Supporting a Gradual Return to Work (TS-GRTW) allows implementation of gradual RTW by facilitating collaboration between employers and workers with MSD-related work disabilities. This tool was adapted for use with workers who have common mental disorders, taking some vulnerability factors into account. The aim is to test the usability of this adapted tool with individuals responsible for disability management in workplaces. Methods A simple descriptive design was retained. Purposive sampling was used to recruit 23 individuals working for at least the previous year in disability management in their workplace. Various types of workplaces were sought. Semi-structured interviews were conducted and transcribed. The verbatim were analyzed based on a qualitative approach.“ Results The participants (6 M, 17 F, mean age 42.6 years, SD 8.1) came from 10 activity sectors, mostly in large companies. Many of them (17/23) saw benefits in using the tool in their organization, as it allows to: 1. standardize the RTW process, 2. delegate tasks to managers and supervisors, 3. structure communication among stakeholders, 4. monitor actions 5. motivate worker engagement, and 6. foster common understanding and legitimization of the process within the organization. A large proportion of participants (17/23) envisaged using the tool. Certain organizational constraints could limit implementation (e.g. lack of time). Discussion Our results support the usability of the adapted TS-GRTW by potential users. However, our results mainly reflect large companies, which often have structures in place. Conclusion The next step would be to test usability with the workers and its implementation.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".