Functional, work-related rehabilitative programming for cancer survivors experiencing cancer-related fatigue
Bibliographic record
Abstract
Introduction Cancer-related fatigue negatively impacts 50–90% of cancer survivors. In North America, approximately 50% of return-to-work interventions initially fail for survivors, with cancer-related fatigue often cited as a barrier to workability. Occupational therapy-driven cancer-related fatigue work-related programming for survivors is sparse, despite many published reviews calling for interdisciplinary interventions; to address work-related performance, specific functional interventions are likely to be needed. Further exploration and a broader understanding of survivors’ cancer-related fatigue management, participation in rehabilitative programmes, and plans for return to work are necessary to target survivor needs better. Method Drawing on social theory, this exploratory descriptive study utilised content and thematic analysis of interviews from 12 survivors to explore and describe the perspectives of survivors experiencing cancer-related fatigue yet desiring to work. Results Content analysis reflected distinct differences in fatigue-related terminology. Thematic analysis identified three themes specific to cancer-related fatigue and workability: valuing physical wellness, perceived cognitive impacts of cancer-related fatigue on function and workability, and the lack of transition from physical exercise to functional work-related activities. Conclusion Survivors identified gaps in care related to managing cognitive symptoms and the need for functional, work-related interventions to manage cancer-related fatigue. With their expertise in function, occupational therapists are well positioned to facilitate work-specific interventions, within cancer-specific exercise programming.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".