Advocacy in Action: Leveraging the Power of Patient Voices to Impact Ovarian Cancer Outcomes in Canada
Bibliographic record
Abstract
Prior to 1997, ovarian cancer (OC) was a 'poor target' for patient advocacy. At that time, there were only three OC researchers in Canada, little information available for women diagnosed, and no community of survivors existed. The Corinne Boyer Fund to advance OC was founded in 1997 (later renamed the National Ovarian Cancer Association (NOCA) and subsequently Ovarian Cancer Canada (OCC)), and a Blueprint for Action was established. NOCA developed training programs for public education, partnered with clinicians and scientists, established a Tissue Banking Network across Canada In 2015, the Ladyballs awareness campaign was launched nationally, giving the community a presence and voice. Strategic planning by the organization put advocacy for research funding as a top priority and, working with patients and researchers across the country, petitioned the government for C$10 million in research funding. In 2019, OCC received the funding. In 2020, the OvCAN project was launched with the aim to improve the outcomes of women diagnosed with OC. In the first three years of OvCAN, a pan-Canadian team of 25 Patient Partners was established, and 41 projects to date on research models, pre-clinical and clinical trials covering a wide spectrum of OC types have been funded.
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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 | 0.026 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".