Procurement-network contributions to healthcare supply chain resilience: a case study from Canada
Bibliographic record
Abstract
Purpose This article investigates how the healthcare sector can reorganize its procurement network to better balance its resilience and cost-minimization objectives. Design/methodology/approach A single case study was conducted on the procurement of personal protective equipment (PPE) during the first COVID-19 pandemic wave in the Quebec public healthcare network. Interviews were conducted with stakeholders from the supply chain management (SCM) departments at eight public healthcare institutions. Findings Two major challenges in the early months of the pandemic impacted the development of resilience in the healthcare network. First, peripheral actors’ decisions, which orient procurement objectives, limited the deployment of resilience measures in the supply chain (SC). Second, SC resilience included hundreds of products other than PPE that are critical to the delivery of care. The article illustrates the challenges of SCR, which will inevitably be accompanied by additional costs when purchasing in the public healthcare sector is often focused on the lowest price. Originality/value Drawing from the network perspective model, this article examines the actions of Quebec supply network stakeholders through the three phases of SCR: anticipation, response to disruption, and recovery. Finally, the article suggests that decision-makers remove the cost of resilience measures from the purchase price of products, in order to maintain these measures over the long term.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".