Think Globally, Act Regionally to Optimize Quality Health System Management
Bibliographic record
Abstract
Initiatives to address the quality in health system management have become a global phenomenon. The complexity of providing quality healthcare is arguably unmatched. From the wealthiest of regions to those in the direst conditions, the efficacy of integrating quality management principles and practices is being accepted by executives, practitioners, and governmental authorities alike. This global level of agreement is based on a wealth of knowledge and experience gained over many decades across a multitude of industries. The challenge is instituting the transformative capability of quality management processes into workable systems that will optimize care across healthcare services globally. This paper is based on the perspective that orienting systems to the delivery and ongoing improvement of quality is fundamental to progress. The authors argue that healthcare leaders can affect lasting change by adopting universally proven precepts of quality enterprise wide and executing them across channels to meet expectations across the spectrum of patient and healthcare worker populations.
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 | 0.032 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".