Bibliographic record
Abstract
Patients, families, and care providers affected by patient safety incidents expect there will be learning and improvement so that others will not suffer. For that, countries need mature data systems and a culture of safety that includes improving by learning from reporting hazards, harm, and near misses, as well as learning from situations and organizations where safe care is delivered consistently over time, which is in most cases. While systems are in place to support incident reporting, sharing, and learning from a variety of sources, in Canada truly national incident reporting is limited to medications, adverse drug reactions, and device failures. However, there are other pan-Canadian and grassroots efforts to advance reporting and learning from patient safety incidents that are complementary. System and contextual factors influence the ability to improve safety, learn, and report. An important one is the COVID-19 pandemic, which resulted in limited or delayed patient safety reporting and some scaling back of improvement projects. The best systems incorporate reporting from multiple sources (patient feedback, coroner reports, etc.) and engage all people involved in care, especially patients and families, in their design, implementation, and continuous improvement. Patient groups, like Patients for Patient Safety Canada (PFPSC), provide the perspective of patients and families with lived experiences that can effectively improve safety. PFPSC contributes to the development of Canadian patient safety strategies, policies, and programs, and innovates and co-leads initiatives that matter to patients and the public. The World Health Organization’s Global Patient Safety Action Plan includes patient safety incident reporting and learning systems to “ensure a constant flow of information and knowledge to drive the mitigation of risk, a reduction in levels of avoidable harm, and improvements in the safety of care” objective.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".