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Record W3123972681 · doi:10.12927/hcq.2020.26398

Shortages of Palliative Care Medications in Canada during the COVID-19 Pandemic: Gambling with Suffering

2021· article· en· W3123972681 on OpenAlexaffvenueabout
José Pereira, Amit Arya, James Downar, Patty Rice, Susan MacDonald, Osborne Ed, Salmaan Kanji, Robert Sauls

Bibliographic record

VenueHealthcare Quarterly · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCanadian Hospice Palliative Care AssociationOttawa HospitalCARE Canada
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Economic shortageMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPalliative careIntensive care medicineMedical emergencyNursingDiseaseGovernment (linguistics)VirologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Patients with serious illnesses such as cancer, advanced organ failure, dementia and COVID-19 rely on medications to alleviate suffering from uncontrolled symptoms. Numerous actual or threatened shortages of key medications used to provide palliation have been reported during the COVID-19 pandemic. This article explores the nature of these shortages, factors that have contributed to them and strategies to mitigate them. It calls on all levels of the healthcare system and the government to address this problem. Shortages in these medications are as serious as shortages in medications used to cure or control diseases.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.047
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
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 head0.318
GPT teacher head0.440
Teacher spread0.122 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

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