Challenges in Knowledge Translation: The Early Years of Cancer Care Ontario’s Program in Evidence-Based Care
Bibliographic record
Abstract
BACKGROUND: Cancer Care Ontario's Program in Evidence-Based Care (pebc) was formalized in 1997 to produce clinical practice guidelines for cancer management for the Province of Ontario. At the time, the gap between guideline development and implementation was beginning to be acknowledged. The Program implemented strategies to promote use of guidelines. METHODS: The program had to overcome numerous social challenges to survive. Prospective strategies useful to practitioners-including participation, transparent communication, a methodological vision, and methodology skills development offerings-were used to create a culture of research-informed oncology practice within a broad community of practitioners.Reactive strategies ensured the survival of the program in the early years, when some within the influential academic community and among decision-makers were skeptical about the feasibility of a rigorous methodologic approach meeting the fast turnaround times necessary for policy. RESULTS: The paper details the pebc strategies within the context of what was known about knowledge translation (kt) at the time, and it tries to identify key success factors. CONCLUSIONS: Many of the barriers faced in the implementation of kt-and the strategies for overcoming them-are unavailable in the public domain because the relevant reporting does not fit the traditional paradigm for publication. Telling the "stories behind the story" should be encouraged to enhance the practice of kt beyond the science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.229 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".