Benchmarking as a tool for the improvement of health services' supply departments
Bibliographic record
Abstract
This paper presents a benchmarking study carried out on the supply departments of Quebec's health services. The paper begins with the definition of a methodology to collect the information needed, both environmental (to enable institutions to be sorted into homogenous groups) and performance related. The analysis of indicators and the data envelopment analysis (DEA) models allowed classification of each hospital's performance and explained the operational approaches used, either at a general level or for each subprocess of the supply chain. It was observed that important economies of scale may be achieved with better co-ordination and with the regrouping of the supplying activities, both for purchasing management and central store management. The study showed that the best performance of central store services comes with flexible administrative structures, by receiving packages as small as possible and by using employees from the lowest range of the hierarchy. Purchasing services should employ highly qualified and well-paid staff. Although such services are relatively small with respect to their purchase volume, they show a higher activity rate. As a result of the discovered performing strategies, the possible economies range from 20% to 30% of the actual supply-chain management cost.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".