SCANNING THE HORIZON IN A DECENTRALIZED HEALTHCARE SYSTEM: THE CANADIAN EXPERIENCE
Bibliographic record
Abstract
Canada has a highly decentralized health care system with 13 provinces and territories delivering health care within their own respective jurisdictions. Decisions regarding which innovative health technologies to adopt are often driven by the unique health care priorities of each jurisdiction's population. To understand these needs, the Canadian Agency for Drugs and Technologies in Health's (CADTH's) Early Awareness Service has expanded its activities. In addition to proactively scanning the horizon for new and emerging health technologies, the Early Awareness Service also scans the horizon for national and jurisdictional health policy issues. This paper looks at CADTH's process for identifying and monitoring policy issues at a national and jurisdictional level. CADTH's Early Awareness Service delivers timely information on emerging health care concerns and technologies that may affect health care finances, facilities, operations, and patient care. The identification of important policy issues can help determine which new and emerging technologies will have the most significant impact on the health care system. The information that CADTH scans can also be used to help decision-makers prepare for potential developments and events that may have an impact on health care systems. By improving its capability to identify and share policy issues across and within jurisdictions, CADTH is better situated to provide information that can be used by policy-makers to help them plan and anticipate for the introduction of new technologies and future developments affecting the unique health care needs of their jurisdictions.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".