A Pan-Canadian Framework for Cancer Survivorship Research
Bibliographic record
Abstract
Background and context: Across all cancer types, two-thirds of Canadians diagnosed with cancer today will survive long-term, reflecting great progress in cancer detection and treatment. Many survivors, however, will experience substantial and long-term impacts of their diagnosis and treatment. Within this context, the Canadian Cancer Research Alliance (CCRA) sought to inform the cancer research funding community on how, and what kinds of research are needed, to enable research that will make a difference to patients as they move from treatment to the posttreatment phase. Aim: To develop and implement a national framework and recommendations to enable coordinated and strategic action among cancer research funders that advances cancer survivorship research in Canada in ways that improve survivors' care and experiences. Strategy/Tactics: Multiple approaches were used to inform framework development: a strategic literature review; an analysis of cancer survivorship research funding from 2005-13; and an online survey and key informant interviews from the broader stakeholder community. An Expert Panel and Patient Advisory Committee were also engaged to provide guidance and feedback. Program/Policy process: Over the course of one year, the project team and a working group of CCRA members met regularly to steer framework development. This involved activities such as developing data collection approaches and tools, reviewing data and emerging findings, and translating findings into priority areas and recommendations. In total, > 200 Canadian and international stakeholders provided input through the survey and interviews. Outcomes: Released March 2017, the Pan-Canadian Framework for Cancer Survivorship Research provides four recommendations for cancer research funders: 1) ensure ongoing and meaningful involvement of cancer survivors; 2) align funding calls with existing needs and potential for impact; 3) create opportunities for the translation of research into practice and policy; and 4) build and maintain infrastructure and expertise to advance research. Specific research priorities were emphasized across three research domains: survivors' experiences and outcomes; late and long-term effects; and models of care. The priorities ranged from investigating the mechanisms of late/long-term effects to conducting intervention research to improve psychosocial outcomes, prevent and ameliorate late effects, and improve integration of follow-up care. What was learned: A broad range of stakeholders came together to develop a national framework to maximize the impact of shared targeted research investment in cancer survivorship research. Survivors' voices were key to agreeing on definitional issues of survivorship, identifying priority research areas, and ultimately lending credibility to the resulting framework. Implementation of the framework is the next step of work for CCRA members. Planning has commenced on identifying an initial priority for joint action.
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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 | 0.104 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.032 |
| Science and technology studies | 0.030 | 0.054 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".