The Cancer and Work Scale (CAWSE): Assessing Return to Work Likelihood and Employment Sustainability After Cancer
Bibliographic record
Abstract
Background: Returning to and sustaining employment after cancer presents significant challenges for individuals touched by cancer (ITBC). While vocational rehabilitation and workplace accommodations are critical, existing return to work (RTW) assessments lack cancer-specific considerations, limiting their clinical and occupational utility. Purpose: This study aimed to develop and validate the Cancer and Work Scale (CAWSE), a psychometrically robust tool designed to assess RTW likelihood and employment sustainability among ITBC, while also providing avenues for targeted interventions. Methods: A two-phase cross-sectional study was conducted. Study I (n = 130) assessed content validity and construct development, leading to a refined 43-item CAWSE. Study II (n = 216) employed exploratory and confirmatory factor analyses to establish structural validity, reliability, and responsiveness. Additional validation included correlations with fatigue, cognitive difficulties, depression, and anxiety. Results: Factor analysis supported a seven-factor structure with 31 final items. The CAWSE demonstrated good internal consistency (α = 0.787), construct validity, and moderate responsiveness (AUC = 0.659). High sensitivity allowed for accurate identification of RTW difficulties, with an established cut-off score of 123.5 on the total CAWSE. Implications: The CAWSE fills a critical gap in oncology-specific vocational rehabilitation, offering healthcare providers a validated tool for targeted interventions to enhance RTW outcomes and long-term employment sustainability for ITBC.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".