Bibliographic record
Abstract
For more than a decade, healthcare organizations across Canada have been using Lean management tools to improve care processes, reduce preventable adverse events, increase patient satisfaction and create better work environments.The largest system-wide effort in Canada, and perhaps anywhere, is currently under way in Saskatchewan.The jury is still out on whether Lean efforts in that province, or elsewhere in Canada, are robust enough to transform current delivery systems and sustain new levels of performance.This issue of Healthcare Quarterly features several articles that provide a perspective on Lean methods in healthcare. For more than a decade, healthcare organizations across Canada have been using Lean management tools to improve care processes, reduce preventable adverse events, increase patient satisfaction and create better work environments.Lean principles and methods focus on engaging staff, providing them with the tools to diagnose and improve care and the patient/client experience, with a focus on reducing waste and creating better value.Many leaders are drawn to Lean methods because they seem like a practical solution to pressing and seemingly intractable problems.For example, beginning in 2009, the Ontario Ministry of Health and Long-Term Care created an Emergency Department Process Improvement Program (ED PIP) to support hospitals in improving ED patient flow and reducing wait times.Eighty-one hospitals in Ontario participated in ED PIP, with many implementing changes that reduced wait times.Results varied, but for many hospitals it was an introduction to a
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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".