Bibliographic record
Abstract
In his review of patient safety issues in the Canadian healthcare system, Dr. Matthew Morgan states that "coordinated national EHR initiatives will cost less, save lives and prevent harm when compared to the status quo." Canada Health Infoway is spearheading this initiative in Canada. Infoway's No. 1 guiding principle for investment is that projects undertaken must "enhance the quality of patient care, healthcare services and patient safety." They must also support the development and adoption of pan-Canadian interoperable EHR solutions. Infoway is working in seven major areas to improve electronic access to accurate and timely health information in order to reduce errors, facilitate accurate diagnoses and speed treatment. These areas include the building blocks of the EHR: infostructure, registries, digital imaging systems, and drug and laboratory information systems. Infoway is also developing and expanding telehealth networks to increase the scope of the Canadian healthcare system. Infoway was recently mandated to develop a public health surveillance system for infectious diseases to give healthcare providers a tool for tracking and managing disease outbreaks in the Canadian population. These systems will improve safety, quality, accessibility, cost-efficiency and the sustainability of the healthcare system. Patient safety is a cornerstone of Infoway's activities.
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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.102 | 0.059 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".