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Record W4210372706 · doi:10.1177/08404704211058414

Supply chain capacity to respond to COVID-19 in Newfoundland and Labrador: An integrated leadership strategy

2022· article· en· W4210372706 on OpenAlexafffundabout
Anne Snowdon, Michael Saunders

Bibliographic record

VenueHealthcare Management Forum · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsUniversity of Windsor
FundersCanadian Institutes of Health Research
KeywordsSupply chainBusinessSupply chain managementHealth careValue chainEconomic shortageCoronavirus disease 2019 (COVID-19)PandemicProduct (mathematics)Demand chainMarketingIndustrial organizationService managementEconomic growthEconomicsMedicine

Abstract

fetched live from OpenAlex

This provincial case study, one of seven conducted as part of a national research program on healthcare supply chain management during COVID-19, focuses on Newfoundland and Labrador (NL). Faced with the destabilization of its traditional supply chain, NL leveraged an existing centralized healthcare supply chain structure to organize its supply chain response to the pandemic. To overcome product shortages, health leaders collaborated with their local business community and industries to source and procure personal protective equipment and create domestic manufacturing capacity for critical supplies. The healthcare supply chain response in NL demonstrates the value of a highly integrated and centralized healthcare supply chain management strategy. It also makes clear the value of a diversified healthcare supply chain, one which draws on local manufacturing capacity to create a domestic source of critical supplies and overcome shortages from global suppliers.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.006
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.073
GPT teacher head0.300
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes3
Has abstractyes

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