Bibliographic record
Abstract
This paper provides evidence that Canada's healthcare system is not as safe as it needs to be, and suggests ways to make it safer. Healthcare leaders must recognize that patient safety is indistinguishable from the delivery of high quality, affordable healthcare, and they must become more knowledgeable about the extent of the patient safety problem in Canada. The creation of a Patient Safety Board, modelled after Canada's Transportation Safety Board, will provide the authority healthcare leaders require to reduce medical errors. Without a national Patient Safety Board we cannot efficiently and effectively identify, quantify and address medical errors in Canada. This paper also urges healthcare leaders to recognize that a fundamental tool in improving patient safety is the electronic health record (EHR). Return on investment data for a national EHR strategy are presented. The author focuses on three EHR initiatives: outpatient electronic prescribing; in-patient computerized physician order entry; and home-based diabetes disease management. Potential net savings to Canada from these three EHR initiatives alone approach $2 billion annually. We must accelerate our EHR investment. Coordinated national EHR initiatives will cost less, save lives and prevent harm when compared to the status quo. These initiatives will also provide the foundation for transforming our healthcare system and will assist in building a better-educated, healthier and therefore more economically competitive nation.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".