Redefining Normal: Experiences of cancer survivors return to work
Bibliographic record
Abstract
Cancer continues to be the primary cause of death in Canada, with an approximate ratio of 2 out of 5 Canadians expected to receive a cancer diagnosis during their lifetime. This research explored cancer patients' experiences with remaining in the workforce while undergoing cancer treatment and/or returning to work upon completion of their treatment. The current five-year net survival rate for all types of cancer is estimated to be 64% (Public Health Agency of Canada, 2022). As survival rates rise, the shortened working lifespan of cancer patients is crucial to consider, given a 1.42-fold higher risk of unemployment compared to the general population (Xu et al., 2023). Despite leading fulfilling lives post treatment, survivors face enduring challenges, including physical, emotional, spiritual, and financial aspects (Public Health Agency of Canada, 2022). According to Xu et al. (2023), joblessness amplifies social isolation, diminishes quality of life, and elevates both individual and societal economic burdens. Individuals were recruited through online surveys. Upon completion of the survey a link was provided for those who wished to volunteer to participate in a telephone interview. Ten cancer survivors volunteered to participate in an interview. Findings from the thematic analysis of the interview data will be presented.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".