Self-efficacy and Perceived Obstacles to Return to Work After Solid Organ Transplantation: Validation of a Tool and Assessment of its Predictive Value for Kidney Transplant Recipients
Bibliographic record
Abstract
Background. Given the risks of long-term disability and job loss in workers on sick leave, quick return-to-work is a primary focus of occupational rehabilitation across health fields. This study sought to identify the return-to-work obstacles and self-efficacy beliefs that predict sick leave duration after kidney transplantation. Given a lack of instruments, the Return-to-Work Obstacles and Self-Efficacy Scale was adapted for Solid Organ Transplantation (ROSES-SOT). Methods. Workers on sick leave recruited across 3 Canadian health centers were administered the ROSES-SOT 0.5–7.3 mo postkidney transplantation (n = 62). Half of the sample was administered the tool again 2 wk later (reliability over time). Workers were then called, and return-to-work dates were collected up to 1 y posttransplantation. Cronbach’s alpha coefficients were calculated (internal consistency), and univariable and multivariable linear regression analyses were performed on sick leave duration. Control variables were age, gender, ethnicity, education, income, disability benefits coverage, manual work, complications, comorbidities, donor, physical and mental health status, and stress about returning to work during the COVID-19 pandemic. Results. The face and content validity of the ROSES-SOT were assessed and deemed satisfactory. Eight of 10 ROSES-SOT dimensions demonstrated satisfactory reliability. COVID-related stress, job demands, fear of relapse, loss of motivation, and organizational injustice predicted sick leave duration. COVID-related stress and organizational injustice remained multivariable predictors. Conclusions. The ROSES-SOT showed adequate reliability and predictive value. Self-efficacy and perceived obstacles could be intervention targets when providing return-to-work support after kidney transplantation. Future studies could investigate the replicability of findings for other transplant types.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".