Improving Patient Safety through a Multi-faceted Internal Surveillance Program
Bibliographic record
Abstract
Improving Care at the Front Lines T he Blueprint for Patient Safety at the Hospital for Sick Children (SickKids) is a 10-item road map that has guided the hospital in its active transition to a culture of safety (Stevens et al. 2005).An essential underpinning of the Blueprint is the ongoing need to identify failures, examine their contributing factors and apply the learnings to processes of care improvement and system redesign with the goal of preventing recurrences.This approach reflects two of the main characteristics of a safety culture: reporting (organizational encouragement for staff to report their errors and near misses) and learning (individuals' and groups' willingness and ability to understand and make changes based on the safety information that is provided through the system) (Reason 1997).Explicitly highlighted in the Blueprint are two key components, internal and external surveillance, that is, the search for potential and existing vulnerabilities and failures in order to put measures in place to avoid and mitigate any harm.Surveillance, "the ongoing systematic collection, analysis and interpretation of healthcare data essential to the planning, implementation, and evaluation of public health practice,
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 | 0.048 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".