Bibliographic record
Abstract
The impact of supply chain integration has been well documented in numerous industries. Healthcare is no exception: Efficient Healthcare Consumer Response> (EHCR) in the late 90s projected US$11 billion in savings in supply chain related costs in the United States alone. However, we believe that external supply chain integration initiatives are drawing most of the attention, while the internal supply chain of hospitals remains the sore spot or weak link in supply chain integration. In view of the many pressures worldwide to reduce the cost of healthcare, as well as the difficulties of adapting healthcare systems to meet the growing needs of an aging population, new illnesses and cures and a severe shortage of nursing staff, this paper offers new management ideas based on case study research of leading international practices to better understand the role and impact of logistics in healthcare. It also presents examples of how to better integrate logistics activities through a unique combination of reengineering and activity-based costing. Indeed, the integration of the internal supply chain can not only bring together new sources of efficiencies in a logistics sense, but it can also impact the quality of care.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".