Bibliographic record
Abstract
Patient Safety Institute (CPSI) has a mandate to make a difference in patient safety through research.Many patient safety research projects funded by CPSI are nearing completion.It is our goal to take the reported findings from these projects and have people learn from the new knowledge on patient safety, as appropriate, make changes and impact healthcare policy and practice.At CPSI, we strive to make sure that the right people find out about the right interventions at the right time.Patient Safety Papers is an important way in which we are sharing research-based results, and we are very proud to co-sponsor this fourth issue.Knowledge transfer is not easy.We are always looking at different methods of communicating with and engaging leaders.CPSI is developing a model for a patient safety knowledge brokerage that anticipates shortening the delay in the uptake of research results to ultimately benefit the health of Canadians.We invite researchers, decision-makers, patients and their families and front-line healthcare providers to accompany us on this journey.We intend to link researchers, decision-makers and healthcare professionals to advance patient safety practices across Canada.Since 2005, we have funded over 50 peer-reviewed research and demonstration projects with a primary focus on patient safety.These 20-month projects must include an interdisciplinary team of researchers and decision-makers, demonstrate a potential for improvements in patient safety and have a strong emphasis on knowledge transfer beyond traditional means.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.126 | 0.023 |
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".