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Record W2016116452 · doi:10.12927/hcq.2013.23040

Ethical Framework for Resource Allocation during a Drug Supply Shortage

2012· article· en· W2016116452 on OpenAlexaffabout
Jennifer Gibson, Sally Bean, Paula Chidwick, Dianne Godkin, Robert Sibbald, Frank Wagner

Bibliographic record

VenueHealthcare Quarterly · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomic shortageScarcityBusinessHealth careContext (archaeology)Scope (computer science)Redistribution (election)MedicineEconomic growthEconomicsPolitical scienceGovernment (linguistics)

Abstract

fetched live from OpenAlex

Drug supply shortages are common in health systems due to manufacturing and other delays. Frequently, shortages are successfully addressed through conservation and redistribution efforts, with limited impact on patient care. However, when Sandoz Canada Inc. announced in February 2012 that it was reducing production of a number of generic injectable drugs at its Quebec facility, the scope and magnitude of the drug supply shortage were unprecedented in Canada. The potential for an extreme scarcity of some drugs raised ethical concerns about patient care, including the need to limit access to some health services. In this article, the authors describe the development and implementation of an ethical framework to promote equitable access to drugs and healthcare services in the context of a drug supply shortage within and across health systems.

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)0.050
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.025
Scholarly communication0.0090.008
Open science0.0020.007
Research integrity0.0130.010
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.061
GPT teacher head0.342
Teacher spread0.281 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations22
Published2012
Admission routes2
Has abstractyes

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