Gaps and Delays in Survivorship Care in the Return-to-Work Pathway for Survivors of Breast Cancer—A Qualitative Study
Bibliographic record
Abstract
Introduction: The number of survivors of breast cancer (bca) in Canada has steadily increased thanks to major advances in cancer care. But the resulting clientele face new challenges related to survivorship. The lack of continuity of care and the side effects of treatment affect the resumption of active life by survivors of bca, including return to work (rtw). The goal of the present article was to outline gaps and delay in survivorship care in the rtw pathway of survivors of bca. Methods: = 5). In an iterative process, a content analysis was performed. Results: The interviews highlighted gaps in survivorship care and the paucity of dedicated resources for cancer survivors. Participants received neither a survivorship care plan nor information about cancer survivorship (for example, transition to a new normal, side effects, rtw). Conclusions: Support for survivors of bca resuming their active lives has to be optimized. We suggest that health professionals have to intervene at 1, 3, and 6 months after cancer treatment. At those points, survivors of bca need support for side-effects management, the rtw decision, resource navigation, and reintegration of daily activities. Also, delay in clinical pathways seems to be longer, and much attention is needed to accompany the transition to a "normal life" after cancer.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".