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Record W4210640847 · doi:10.1177/08404704211061223

Supply chain integration as a strategy to strengthen pandemic responsiveness in Nova Scotia

2022· article· en· W4210640847 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
KeywordsNova scotiaSupply chainBusinessProcurementCLARITYPandemicSupply chain managementHealth careDiversification (marketing strategy)Coronavirus disease 2019 (COVID-19)MarketingMedicineEconomic growthEconomicsGeography

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 Nova Scotia. During the first wave of the pandemic, Nova Scotia faced the massive destabilization of its traditional supply channels and had to grapple with role clarity and communication in its emergency response structure. Nova Scotia was able to centralize its pandemic sourcing, procurement, and management efforts to its provincial health authority. Healthcare supply chain teams were able to rapidly modify their sourcing and procurement processes in order to compensate for the destabilization of their standard supply channels and assume responsibility for the province-wide management and distribution of pandemic supplies. The Nova Scotia case findings make clear both the value of a centralized and dedicated healthcare supply chain response-that integrates all provincial care delivery organizations-and the diversification of the healthcare 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 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 score0.223
Threshold uncertainty score0.448

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.003
Scholarly communication0.0050.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.289
Teacher spread0.259 · 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

Citations17
Published2022
Admission routes3
Has abstractyes

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