Work functioning trajectories in cancer patients: Results from the longitudinal Work Life after Cancer (WOLICA) study
Bibliographic record
Abstract
More than 60% of cancer patients are able to work after cancer diagnosis. However, little is known about their functioning at work. Therefore, the aims of this study were to (1) identify work functioning trajectories in the year following return to work (RTW) in cancer patients and (2) examine baseline sociodemographic, health-related and work-related variables associated with work functioning trajectories. This longitudinal cohort study included 384 cancer patients who have returned to work after cancer diagnosis. Work functioning was measured at baseline, 3, 6, 9 and 12 months follow-up. Latent class growth modeling (LCGM) was used to identify work functioning trajectories. Associations of baseline variables with work functioning trajectories were examined using univariate and multivariate analyses. LCGM analyses with cancer patients who completed on at least three time points the Work Role Functioning Questionnaire (n = 324) identified three work functioning trajectories: "persistently high" (16% of the sample), "moderate to high" (54%) and "persistently low" work functioning (32%). Cancer patients with persistently high work functioning had less time between diagnosis and RTW and had less often a changed meaning of work, while cancer patients with persistently low work functioning reported more baseline cognitive symptoms compared to cancer patients in the other trajectories. This knowledge has implications for cancer care and guidance of cancer patients at work.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".