Promoting resilience in work rehabilitation: development of a transdiagnostic intervention
Bibliographic record
Abstract
Purpose The aim of this study was to develop an operationalized transdiagnostic resilience-based intervention for workers at risk of long-term work disability.Methods A sequential mixed method design was used. Expert clinicians (n = 10) first answered a questionnaire including closed and open-ended questions on the clarity, applicability, relevance and exhaustiveness of a preliminary resilience intervention developed from evidenced-informed resilience factors to prompt reflection. Second, proposals from the questionnaire were discussed at a consensus group meeting with the same experts, yielding a final and improved intervention. Third, semi-structured interviews with work-disabled workers (n = 6) explored the intervention’s acceptability to them. Thematic analysis of the verbatim was performed.Results Experts identified 15 statements on clarity, applicability, relevance or exhaustiveness in the questionnaire that did not achieve consensus and generated 41 modification proposals. The consensus group adopted 15 modifications. The adapted intervention was well-accepted by the workers who had completed a work rehabilitation program. They perceived the intervention as positive, relevant, coherent, useful and consistent with their values.Conclusion A new transdiagnostic resilience intervention in work rehabilitation is available and was on exploratory basis seen acceptable by workers. Next step would be to validate it at a larger scale with more workers and other stakeholders.IMPLICATIONS FOR REHABILITATIONPromoting workers resilience in work rehabilitation fosters a holistic approach in clinical practice.Resilience interventions should be integrated into work rehabilitation programs.A new transdiagnostic resilience intervention designed to complement current work rehabilitation programs is available.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".