The Complementary Medicine Education and Outcomes (CAMEO) Program: Taking the knowledge to the bedside
Bibliographic record
Abstract
Complementary medicine (CAM) use has become part of the care experience for Canadians living with cancer. A growing body of research has demonstrated that decision making about CAM within cancer treatment and care is complex and often overwhelming for patients. Patients require support from the interprofessional health care team to effectively make safe decisions about CAM. An interprofessional team of researchers and clinicians have collaborated at the BC Cancer Agency to develop an innovative program to address patient and health care professional needs related to CAM decision making within the cancer experience. The purpose of this presentation is to describe the recent development of the Complementary Medicine Education and Outcomes (CAMEO) Program at the BC Cancer Agency in Vancouver, BC. The objectives of the CAMEO Program as well as the planned knowledge translation activities related to CAM information and decision support will be discussed. The CAMEO Program is a 4-year program that aims to: 1) support people with cancer in making CAM decisions; 2) strengthen health professionals’ knowledge and clinical skills related to CAM; and 3) facilitate the development of new CAM research knowledge. To achieve these goals, a variety of knowledge translation projects are under development, including continuing education programs for both patients and health professionals, one-on-one counselling for patients with complex needs related to CAM, and clinical tools and guidelines to assist health professionals in supporting safe and informed decisions about CAM. With the predominance of CAM use within cancer populations, the CAMEO program is an important step in the knowledge translation process that will “take the knowledge to the bedside” and provide support to cancer patients in making safe and evidence-informed decisions about CAM.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".