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Record W3155695872 · doi:10.1177/08404704211003610

Leadership for a climate resilient, net-zero health system: Transforming supply chains to the circular economy

2021· article· en· W3155695872 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealthcare Management Forum · 2021
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsGreenfield Research (Canada)
Fundersnot available
KeywordsCircular economyBusinessSupply chainResilience (materials science)Government (linguistics)Order (exchange)ProcurementCarbon footprintClimate changeProduct (mathematics)Industrial organizationEnvironmental resource managementGreenhouse gasMarketingEconomicsFinance

Abstract

fetched live from OpenAlex

The global pandemic has taught us that we can focus the attention of the healthcare system on a clear intention when there is a looming threat. Climate action is required from multiple stakeholders particularly private sector suppliers in order to achieve the net-zero carbon emission by 2050 goal established by the Canadian government. Also building climate resilience among healthcare institutions and their supply chains is urgently needed, as they are already affected by a changing climate. By adopting a circular economy framework, the industry can move away from the current damaging take, make waste economic model and adopt a more sustainable model characterized by designing out waste and pollution, keeping products and materials in use, and regenerating natural systems. Health leaders can adopt sharing platforms, view product as a service, reduce single use products, encourage extended producer responsibility, and encourage value-based procurement in order to further these aims.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.874
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.299
Teacher spread0.245 · 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