Mobilizing Clinician Voices to Manage Health Supply Chain Disruptions Vital to Safe and Quality Patient Care
Bibliographic record
Abstract
This study examines the role of clinicians during supply chain disruptions and the impact of these disruptions on their capacity to deliver care to patients. Clinician leaders (physicians, nurses, pharmacists and regional health authority leaders) from seven Canadian provinces (Ontario, Alberta, British Columbia, Nova Scotia, Newfoundland and Labrador, Quebec and Manitoba) participated in co-design sessions to identify strategies to integrate frontline clinical expertise into supply chain management. A workgroup led by two clinician leaders (a physician and a nurse) defined the challenges of supply disruptions for clinicians (individuals delivering clinical care to patients, such as physicians, nurses and pharmacists) and identified the structural barriers that limit clinician participation in managing supply disruptions and in adapting care delivery through alternative care pathways and resource allocation. This paper presents a set of actionable clinician-led strategies to engage clinicians in supply chain management to ensure that clinician expertise informs supply management decisions and enables safe and quality patient care that is accessible when and where needed. Strategies include designating agencies responsible for clinician communication during supply shortages, building bilateral communication channels linking clinicians and system leaders, implementing standardized communication protocols to engage the workforce in supply chain management and mobilizing clinical expertise to inform supply disruption decisions.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".