Developing a collaborative and sustainable return to work program for employees with common mental disorders: a participatory research with public and private organizations
Bibliographic record
Abstract
PURPOSE: To disentangle the key steps of the return to work (RTW) process and offer clearer recovery-focused and sustainable RTW for people on sick leave due to common mental disorders (CMDs). METHODS: = 26) with each member of the advisory committee in each organization, as well as with employees who had recently experienced CMDs. The interviews examined the RTW process for employees on sick leave due to CMDs as well as RTW stakeholders' perceptions of barriers and facilitators. A thematic approach was used to synthesize the data, following which, results were discussed with the two advisory committees to identify solutions considering key RTW steps. RESULTS: Ten common key steps within the three RTW phases emerged from the semi-structured interviews with RTW stakeholders and discussions with the two advisory committees: 1) At the beginning of sickness absence and involvement of disability management team (phase 1), we found 3 steps (e.g., taking charge of the file), 2) during the involvement in treatment rehabilitation with health professionals and preparation of the RTW (phase 2), 4 steps (e.g., RTW preparation), and finally 3) the RTW and follow-up (phase 3) consists of 3 steps (e.g., gradual RTW). CONCLUSION: A participatory study involving RTW stakeholders helped identify 10 common key steps within three phases to support RTW sustainability of people with CMDs. Future research will need to address how RTW coordinators intervene in the RTW process of employees with CMDs within these steps.IMPLICATIONS FOR REHABILITATIONRehabilitation professionals will benefit from a detailed description of the RTW process (10 steps spread out over 3 RTW phases), allowing them to standardize it while adopting a personalized approach for the employee on sick leave.Rehabilitation professionals are informed of stakeholders' role and actions required in the RTW process; as such the communication between RTW stakeholders should be improved.RTW coordinators will be able to tailor more precisely their intervention, considering the detailed RTW process and RTW stakeholders' role and actions, and thus will become the pivot occupational health specialists for the RTW process.
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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 | 0.040 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".