Canadian Integrative Oncology Research Priorities: Results of a Consensus-Building Process
Bibliographic record
Abstract
BACKGROUND: In Canada, many diverse models of integrative oncology care have emerged in response to the growing number of cancer patients who combine complementary therapies with their conventional medical treatments. The increasing interest in integrative oncology emphasizes the need to engage stakeholders and to work toward consensus on research priorities and a collaborative research agenda. The Integrative Canadian Oncology Research Initiative initiated a consensus-building process to meet that need and to develop an action plan that will implement a Canadian research agenda. METHODS: A two-day consensus workshop was held after completion of a Delphi survey and stakeholder interviews. RESULTS: FIVE INTERRELATED PRIORITY RESEARCH AREAS WERE IDENTIFIED AS THE FOUNDATION FOR A CANADIAN RESEARCH AGENDA: EffectivenessSafetyResource and health services utilizationKnowledge translationDeveloping integrative oncology models Research is needed within each priority area from a range of different perspectives (for example, patient, practitioner, health system) and in a way that reflects a continuum of integration from the addition of a single complementary intervention within conventional cancer care to systemic change. Strategies to implement a Canadian integrative oncology research agenda were identified, and working groups are actively developing projects in line with those strategic areas. Of note is the intention to develop a national network for integrative oncology research and knowledge translation. CONCLUSIONS: The identified research priorities reflect the needs and perspectives of a spectrum of integrative oncology stakeholders. Ongoing stakeholder consultation, including engagement from new stakeholders, is needed to ensure appropriate uptake and implementation of a Canadian research agenda.
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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 | 0.238 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".