Shaping Policy: The Canadian Cancer Society and the Hormone Receptor Testing Inquiry
Bibliographic record
Abstract
BACKGROUND: In 2007, the Government of Newfoundland and Labrador established the Commission of Inquiry on Hormone Receptor Testing to examine problems with estrogen and progesterone hormone receptor tests conducted in the province between 1997 and 2005. Using the Inquiry as a case study, we examine the knowledge transfer activities used by the Canadian Cancer Society - Newfoundland and Labrador Division (CCS-NL) to shape policy and improve cancer control in the province. IMPLEMENTATION: CCS-NL established a panel to advise its legal counsel and asked academic researchers to prepare papers to submit to the Commission. CCS-NL also interviewed patients to better inform its legal arguments, used its province-wide networks to raise awareness of the Inquiry, and provided a toll-free number that people could call. It also provided basic information, resources, and contact information for people who were affected by the flawed hormone receptor tests. The effectiveness of CCS-NL's activities is reflected by the inclusion of its key messages in the Commission's recommendations, and the investment in cancer care following the Inquiry. DISCUSSION: The success of the CCS-NL knowledge transfer efforts stemmed from its reputation as an advocate for cancer patients and its long-standing relationship with researchers, especially at the local level. The case illustrates real-world application of knowledge transfer practices in the development of public policy, and describes how community-based non-government organizations can identify and draw attention to important issues that otherwise might not have been addressed.
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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 | 0.036 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.041 | 0.048 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 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".