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Record W3213606863 · doi:10.3390/medsci9040068

Post-Coronavirus Disease-2019 (COVID-19): Toward a Severe Multi-Level Health Crisis?

2021· article· en· W3213606863 on OpenAlexafffund
Abdelaziz Ghanemi, Mayumi Yoshioka, Jonny St‐Amand

Bibliographic record

VenueMedical Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesFonds de Recherche du Québec - SantéMinistère de l'Éducation et de l'Enseignement supérieur
KeywordsPandemicCoronavirus disease 2019 (COVID-19)MedicineHealth careHealthcare systemCoronavirusDisease2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Economic growthInfectious disease (medical specialty)VirologyPathologyEconomics

Abstract

fetched live from OpenAlex

There were already numerous challenges facing the healthcare system prior to the ongoing coronavirus disease-2019 (COVID-19) pandemic. Although we look forward to ending this pandemic, it is still expected that the healthcare system will face further challenges leading to a multi-level health crisis. Indeed, after the COVID-19 pandemic, there will still be COVID-19 active cases and those left with health problems following COVID-19 infection who will be of a particular impact. In addition, we also have the health problems that either emerged or worsened during COVID-19, especially with the reduced ability of the healthcare system to take care of many non COVID-19 patients during the COVID-19 pandemic. Such expected evolution of the situation highlights the necessity for the decision-makers to consider applying serious reforms and take quick measures to prevent a post-COVID-19 health crisis.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.002

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 head0.363
GPT teacher head0.512
Teacher spread0.149 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations8
Published2021
Admission routes2
Has abstractyes

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