The association of cancer-related fatigue on the social, vocational and healthcare-related dimensions of cancer survivorship
Bibliographic record
Abstract
BACKGROUND: Cancer-related fatigue (CRF) is well documented in cancer survivors, but little is known about the personal and societal impact of CRF. This study aimed to examine the impact of CRF in relation to social and vocational functioning and health care utilization in a large sample of post-treatment cancer survivors. METHODS: We conducted a cross-sectional descriptive study of early stage breast and colorectal cancer survivors (n = 454) who were within 5 years from treatment completion. Social difficulties (SDI-21), work status, absenteeism and presenteeism (WHO-HPQ) and healthcare utilization (HSUQ) were compared in those with (CFR +) and without (CRF -) clinically significant fatigue (FACT-F ≤ 34). RESULTS: A total of 32% met the cut-off criteria for CRF (≤ 34). Participants with CRF + had significantly higher scores on the SDI-21 across all domains and 55% of CRF + vs. 11% in CRF - was above the SDI cut-off (> 10) for significant social difficulties. Participants with CRF + were 2.74 times more likely to be unemployed or on leave (95% CI 1.62, 4.61, p < 0.001). In the subgroup of participants who were currently working (n = 249), those with CRF + reported working on average 27.4 fewer hours in the previous 4 weeks compared to CRF - (p = 0.05), and absolute presenteeism was on average 13% lower in the CRF + group (95% CI 8.0, 18.2, p < 0.001). Finally, individuals with CRF + reported significantly more physician (p < 0.001), other health care professional (p = 0.03) and psychosocial visits (p = 0.002) in the past month. CONCLUSIONS AND IMPLICATIONS FOR CANCER SURVIVORS: CRF is associated with substantial disruption in social and work role functioning in the early transitional phase of cancer survivorship. Better management of persistent CRF and funding for the implementation of existing guidelines and recommended evidence-based interventions are urgently needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".