Proposed Consensus-Based Canadian Integrative Oncology Research Priorities
Bibliographic record
Abstract
Objectives: An increasing number of integrative oncology programs are being established across Canada that offer a combination of complementary and conventional medical treatments in a shift towards whole-person cancer care. It was our objective to identify consensus-based research priorities within a coherent research agenda to guide Canadian integrative oncology practice and policy moving forward.Methods: Members of the Integrative Canadian Oncology Research Initiative and the Ottawa Integrative Cancer Centre organized a 2-day consensus workshop, which was preceded by a Delphi survey and stakeholder interviews.Results: Eighty-one participants took part in Round 1 of the Delphi survey, 52 in Round 2 (66.2%) and 45 (86.5%) in Round 3. Nineteen invited stakeholders participated in the 2-day workshop held in Ottawa, Canada. Five inter-related priority research areas emerged as a foundation for a Canadian research agenda: Effectiveness; Safety; Resource and Health Services Utilization; Knowledge Translation; and Developing Integrative Oncology Models. Research is needed within each priority area from a range of different perspectives (e.g., patient, practitioner, health system) and that reflects a continuum of integration from the addition of a single complementary intervention within conventional cancer care to systemic change. Participants brainstormed strategic directions to implement the developing research agenda and identified related opportunities within Canada. A voting process helped to identify working groups to pursue strategic directions within the interest and expertise of meeting participants.Conclusion: 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 the 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 | 0.324 | 0.267 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.036 | 0.014 |
| Open science | 0.019 | 0.027 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".