PEARLS for Systems Integration
Bibliographic record
Abstract
STATEMENT: Modern healthcare organizations strive for continuous improvement in systems and processes to ensure safe, effective, and cost-conscious patient care. However, systems failures and inefficiencies lurk in every organization, often emerging only after patients have experienced harm or delays. Simulation and debriefing, focused on identifying systems gaps, can proactively lead to improvements in safety and quality. Systems-focused debriefing requires a different approach than traditional, learner-focused debriefing. We describe PEARLS for Systems Integration, a conceptual framework, debriefing structure and script that facilitators can use for systems-focused debriefing. The framework builds on Promoting Excellence And Reflective Learning in Simulation, using common debriefing strategies (plus/delta, focused facilitation, and directive feedback) in a modified format, with new debriefing scripts. Promoting Excellence And Reflective Learning in Simulation for System Integration offers a structured framework, adaptable for debriefing systems-focused simulations, to identify systems issues and maximize improvements in patient safety and quality.
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 | 0.035 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.172 | 0.086 |
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".