O-159 INSIGHTS INTO SMALL AND MEDIUM ENTERPRISES PRACTICES OF EARLY RETURN TO WORK WITH MODIFIED TASKS FOLLOWING A WORK-RELATED INJURY
Bibliographic record
Abstract
Abstract Introduction Facilitating early return-to-work with modified tasks following work-related injuries is a common practice in industrial countries to promote workers’ rehabilitation. This presentation aims to explore how that strategy is implemented in small and medium enterprises (SMEs) which often experience higher rates of occupational injuries compared to larger corporations. Methods Using a qualitative descriptive design, semi-structured interviews were conducted with managers, while focus groups involved external stakeholders in disability management. The selection of participants was purposeful, aiming to ensure diversity in roles and perspectives. Qualitative content analysis of verbatim transcripts was performed, using a mix-coding approach that integrated both pre-established and emerging topics. Results SMEs have limited options for task modifications tailored to the injured worker’s health condition, abilities and interests. Modified tasks are typically chosen informally by the person in charge of the disability management in the enterprise with the supervisor’s insight, and occasionally the worker’s depending on the company. While follow-ups by a manager are common, they are not consistently standardized, placing the responsibility on workers to raise concerns in case of difficulties. Discussion The informal approach of the early return-to-work with modified tasks, sought-after by SMEs for its flexibility, carries the risk for workers to exceed their limitations, potentially aggravating their injury. The contribution of a healthcare professional throughout the process can reduce these risks and improve rehabilitation. Conclusion While early return-to-work with modified tasks strategies can be beneficial for both SMEs and the injured workers, integrating safeguards can ensure that the worker’s rehabilitation remains at the forefront of the process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".