Canadian Resources, Programs, and Models of Care to Support Cancer Survivors’ Transition beyond Treatment: A Scoping Review
Bibliographic record
Abstract
(1) Background: One in two Canadians will be diagnosed with cancer in their lifetime, but as a result of the progress in diagnosis and treatment, more individuals are surviving cancer than ever before. However, the impact of cancer does not end with treatment. The objectives of this review are to (1) provide a broad overview of the supportive care interventions and models of care that have been researched to support Canadian post-treatment cancer survivors; and (2) analyze how these supportive care interventions and/or care models align with the practice recommendations put forth by Cancer Care Ontario (CCO) and the Canadian Association of Psychosocial Oncology/Canadian Partnership Against Cancer (CAPO/CPAC). (2) Methods: An electronic search was completed in MEDLINE, Embase, PsycINFO, and CINAHL in January 2021. Included studies described supportive care interventions or models of care utilized by adult Canadian cancer survivors. (3) Results: Forty-two articles were included. Survivors utilized a multitude of supportive care interventions, with peer support and physical activity programs being most frequently cited. Four models of follow-up care were identified: primary care, oncology care, shared-care, and transition clinics. The supportive care interventions and models of care variably aligned with the recommendations set by CCO and CAPO/CPAC. The most commonly followed recommendation was the promotion of self-management and quality resources for patients. (4) Conclusions: Results indicate an inconsistency in access to supportive care interventions and the delivery of survivorship care for cancer survivors across Canada. Current efforts are being made to implement the recommendations by CCO and CAPO/CPAC; however, provision of these guidelines remains varied.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.034 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".