Returning to work after cancer: Survivors', caregivers', and employers' perspectives
Bibliographic record
Abstract
OBJECTIVE: The Return to Work Initiative was launched to build a comprehensive understanding of issues, needs, current resources, and available supports for Canadian cancer survivors returning to work as the basis for developing a national action plan. METHODS: This Initiative drew on perspectives of stakeholders through a survey and consultations with cancer survivors and caregivers to learn about challenges regarding return to work and interviews and focus groups with workplace representatives and employers to determine issues encountered in the workplace. Common perspectives across stakeholder groups were identified. RESULTS: Cancer survivors (n = 410) described reduction in income, positive and negative experiences returning to work, and work-related issues regarding side effects. Caregivers (n = 60) described loss of concentration and productivity, stress, and lack of support from coworkers. Employer representatives (n = 68) revealed challenges for managers knowing how best to support cancer survivors as there are few of them of which they are aware. All stakeholders agreed that returning to work for cancer survivors is challenging. Multiple strategies are needed to achieve success: in-depth understanding of the issues, consideration of accommodation, communication among stakeholders, education, resources, and financial support. CONCLUSIONS: The work provided a foundation for making decisions about how to proceed to improve return to work for Canadian cancer survivors.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".