CHSRF Knowledge Transfer: Myths, "Zombies" and "Damned Lies" Plague Canadian Healthcare Systems: What's a Researcher to Do?
Bibliographic record
Abstract
seasoned researchers and today's research savvy decision-makers, some of the ideas that show up in the news media or come up over coffee are laughable.That's because some of the ideas -for example, that the aging population will overwhelm the healthcare system -have long been discredited in health services research discussions.And, yet, the spread of such myths is no laughing matter.For example, when the public hears that all of our Canadian-trained doctors are headed to the United States or that our systems are financially unsustainable, they likely worry that their health is at stake and their systems are in disrepair.Decision-makers -even those with a savoir faire for making evidence-informed decisions -face similar challenges.Using research to inform management and policy is already difficult notwithstanding when popular culture supports measures that are counterintuitive to the best research.
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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 | 0.088 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.057 | 0.114 |
| Scholarly communication | 0.038 | 0.024 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 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".