The Strategies Used to Balance Health and Work Across the Solid Organ Transplantation Trajectory
Bibliographic record
Abstract
Introduction: Workers who undergo solid organ transplantation report frequent conflicts between the unpredictable demands of their health condition and the rigid requirements of their employer and of health services. The present study aimed to describe the self-management strategies adopted by workers while staying at work before transplantation and during sustainable return-to-work posttransplantation. Methods: Fifteen employed kidney, liver, and lung transplant recipients were recruited from 2 large urban university health centers in Montreal, Canada. Three focus groups were held, and thematic analysis was performed. Findings: Seven strategies were identified: responding promptly and consistently to fatigue-related needs, planning ahead with immediate supervisors while remaining strategic about when to disclose transplantation, requesting work accommodations, requesting flexibility in healthcare provision, consulting physicians about work-related issues, informing co-workers about work limitations and immunosuppression and asking not to be treated differently in the workplace. Conclusion: Access to work accommodations, support from physicians and flexibility in treatment and appointment schedules supported workers’ ability to manage their health while staying at work before and after undergoing solid organ transplantation. In light of findings, it may be useful for healthcare professionals to address workers’ concerns about work limitations and work accommodation implementation, especially when the illness-management burden increases before transplantation and during posttransplantation sick leave. Future studies could describe the strategies used by other important stakeholders when attempting to provide support to workers.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".