Development of a return-to-work planning tool for cancer survivors
Bibliographic record
Abstract
BACKGROUND: The ability to return to work (RTW) after a cancer diagnosis can present significant challenges for survivors and can be an important predictor of their long-term quality of life. Survivors report concerns related to disclosing their cancer diagnosis, describing ongoing late effects of cancer, and negotiating workplace accommodations. PURPOSE: This paper outlines the development of an RTW planning template (RTW-PT) designed to improve communication with stakeholders involved in the RTW process. Lessons learned throughout the process of developing the RTW-PT and implications for clinical practice are presented. KEY ISSUES: The RTW-PT assists the survivor and his or her health care provider to prioritize job demands during a graded RTW and to identify potential accommodations. The RTW-PT also helps survivors plan how they will communicate their RTW needs, particularly with employers and insurance representatives. IMPLICATIONS: The RTW-PT offers a structured approach to support communication among stakeholders involved in the RTW process and to assist survivors in negotiating workplace accommodations.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".