Bibliographic record
Abstract
Due to the diversity of its players, the American healthcare sector has experimented with different types of integrated supply chain management systems for medical supplies. In the 1980s, US distributors were offering customers the so‐called stockless replenishment method, whereby the distributor picks and packs products according to the particular needs of each patient care unit and, in most cases, delivers them directly. By the late 1990s, stockless agreements had run out of steam, as distributors sought to optimize the balance between their efforts expended in hospital replenishment and the hospitals’ inventory savings. Among the various reflections and initiatives aimed at finding such a new balance, we focused on the experience of a Quebec (Canada) hospital adopting a hybrid version of the stockless system, under which the distributor supplied high‐volume products for the patient care unit in case quantities, leaving the institution’s central stores to break down bulk purchases of low‐volume products into point‐of‐use format (eaches). The study reveals marginal benefits from the hybrid method for both the institution and the distributor. However, it also reveals the importance of the manufacturer’s role with respect to packing formats, and demonstrates that the rearrangement of storage areas can generate substantial savings, opening the way to means for improving the healthcare sector supply chain.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".