Work‐specific cognitive symptoms and the role of work characteristics, fatigue, and depressive symptoms in cancer patients during 18 months post return to work
Bibliographic record
Abstract
OBJECTIVE: Cancer patients can experience work-specific cognitive symptoms post return to work. The study aims to (1) describe the course of work-specific cognitive symptoms in the first 18 months post return to work and (2) examine the associations of work characteristics, fatigue and depressive symptoms with work-specific cognitive symptoms over time. METHODS: This study used data from the 18-month longitudinal "Work Life after Cancer" cohort. The Cognitive Symptom Checklist-Work Dutch Version (CSC-W DV) was used to measure work-specific cognitive symptoms. Linear mixed models were performed to examine the course of work-specific cognitive symptoms during 18-month follow-up; linear regression analyses with generalized estimating equations were used to examine associations over time. RESULTS: Working cancer patients examined with different cancer types were included (n = 378). Work-specific cognitive symptoms were stable over 18 months. At baseline, cancer patients reported more working memory symptoms (M = 32.0; CI, 30.0-34.0) compared with executive function symptoms (M = 19.3; CI, 17.6-20.9). Cancer patients holding a job with both manual and nonmanual tasks reported less work-specific cognitive symptoms (unstandardized regression coefficient b = -4.80; CI, -7.76 to -1.83) over time, compared with cancer patients with a nonmanual job. Over time, higher depressive symptoms were related to experiencing more overall work-specific cognitive symptoms (b = 1.27; CI, 1.00-1.55) and a higher fatigue score was related to more working memory symptoms (b = 0.13; CI, 0.04-0.23). CONCLUSIONS: Job type should be considered when looking at work-specific cognitive symptoms over time in working cancer patients. To reduce work-specific cognitive symptoms, interventions targeted at fatigue and depressive symptoms might be promising.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".