Bibliographic record
Abstract
To fulfill its commitment to patient safety, the Institute for Safe Medication Practices Canada (ISMP Canada) receives information about preventable adverse drug events from individual health care practitioners and health service organizations. Reporters send the information voluntarily and ISMP Canada uses its analysis of the medication incident reports to develop recommendations for enhancing patient safety. In 2005, ISMP Canada was part of a research team1 that obtained funding from the Canadian Patient Safety Institute to conduct a scan of legislation from across Canada that might apply to the reporting (or, in the term used by most statutes, “disclosures”) of medication incident data to external organizations, such as ISMP Canada. We used the term “sharing” to refer to external incident reporting that is voluntary. Although the same statutory rules might apply to all kinds of incident reporting, we focused on types of information that are specific to medication incidents. Reporters and reporting organizations in various provinces often ask questions about the landscape of privacy legislation across Canada and about the limits that such legislation might place on the sharing of medication incident data. As it happens, some of the key messages that came out of our legislative scan may be helpful to reporters with questions about privacy, confidentiality and the sharing of incident data.
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 | 0.147 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".