Understanding the Post-Treatment Concerns of Cancer Survivors with Five Common Cancers: Exploring the Alberta Results from the Pan-Canadian Transitions Study
Bibliographic record
Abstract
As the rates of cancer incidence and survival increase in Canada, more patients are living in the post-treatment survivorship phase of their cancer journey. Identifying cancer survivors' concerns and unmet needs is important so that health care teams can provide relevant information, supports, and resources. Secondary data analysis was carried out on the Alberta patient sample from the 2016 Pan-Canadian Transitions Study survey, designed by the Canadian Partnership Against Cancer. The top concerns for patients treated for five different cancers were examined descriptively and compared. A question about information that patients received post-treatment was also descriptively analyzed. Binary logistic regressions were conducted for each tumour group, using the top three concerns for each group as outcomes and a variety of demographic factors as independent variables. There were 1833 valid respondents in the Alberta sample. Fatigue and anxiety were top concerns for multiple tumour groups. Most patients received more information about treatment side effects than about signs of recurrence and community resources. Within certain tumour groups, younger patients had higher odds of having concerns, particularly anxiety. Awareness of the common and unique concerns experienced by cancer survivors post-treatment enables health care providers to tailor care and resources to help patients manage their symptoms and concerns. These findings address gaps in knowledge around the cancer survivorship phase and may be applicable to cancer programs and primary care providers in Alberta and beyond.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".