Understanding Cancer Survivors’ Needs and Experiences Returning to Work Post-Treatment: A Longitudinal Qualitative Study
Bibliographic record
Abstract
BACKGROUND: This study aimed to understand Canadian cancer survivors' experiences during the return-to-work (RTW) process. METHODS: A prospective qualitative longitudinal design was employed using the principles of phenomenological inquiry. Cancer survivors took part in three in-depth interviews: at the end of treatment, and 3 and 9 months after the first interview. Transcripts were analyzed using constant comparative analysis, guided by the Cancer and Work model. RESULTS: A total of 38 in-depth interviews were conducted with 13 participants. The resultant themes were: (1) supports received or desired to enable RTW; (2) others' limited understanding of the long-term impacts of a cancer diagnosis and its treatment; (3) worries and self-doubts about returning to work; and (4) changing perspectives on life and work after cancer. CONCLUSIONS: Cancer patients returning to work after treatment often experience challenges throughout the process, including varying levels of support from others and a range of ongoing effects and motivation to RTW. There is a clear gap in terms of the professional supports available to these individuals. Future research should focus on investigating how to improve both quality and accessibility of supports in a way that is personalized to the individual.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| 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".