The Role of Co-Workers in the Return-to-Work Process
Bibliographic record
Abstract
There is a large body of research examining work disability management and the return to work (RTW) of sick or injured workers. However, although this research makes clear the roles of the returning worker and supervisor, that of the co-workers is less well understood. To increase understanding of this topic, we have identified, reviewed, and discussed three studies that emerged from our connection with a Canadian research-training program. The first study, conducted in Sweden by Tjulin, MacEachen, and Ekberg (2009), showed that co-workers can play a positive role in RTW, but this is often invisible to supervisors. The second study, undertaken by Dunstan and MacEachen (2013) in Canada, found that RTW could both positively and negatively impact co-workers. For instance, co-workers may benefit from learning new skills, but may also be burdened by the need to assume extra work to accommodate a returning worker. The third study, performed in Belgium by Mortelmans and Verjans (2012) and Mortelmans, Verjans, and Mairiaux (2012) reported the need to include the expectations and objections of co-workers in RTW plans and implemented a three-step RTW tool that involves co-workers. Taken together, these studies highlight the social context of work, the positive role played by co-workers in the RTW process, the impacts of workplace social relations on RTW outcomes, and the benefits to all of involving co-workers in RTW plans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".