MétaCan
Menu
← Back to cohort
Record W4402581148 · doi:10.1007/978-3-031-61939-7_1

Global Challenges After a Global Challenge: Lessons Learned from the COVID-19 Pandemic

2024· article· en· W4402581148 on OpenAlexaff
Niloufar Yazdanpanah, Constantine Sedikides, Hans D. Ochs, Carlos A. Camargo, Gary L. Darmstadt, Artemi Cerdà, Valentina Cauda, Godefridus J. Peters, Frank W. Sellke, Nathan D. Wong, Elisabetta Comini, A. Ruiz-Jimeno, Vivette Glover, Nikos Hatziargyriou, Christian E. Vincenot, Stéphane Bordas, Idupulapati M. Rao, Hassan Abolhassani, Gevork B. Gharehpetian, Ralf Weiskirchen, Manoj Gupta, Shyam Singh Chandel, Bolajoko O. Olusanya, Bruce D. Cheson, Alessio Pomponio, Michael Tänzer, Paul S. Myles, Wen‐Xiu Ma, Federico Bella, Saeid Ghavami, S. Moein Moghimi, Domenico Praticò, Alfredo Martínez Hernandez, María Martínez‐Urbistondo, Diego Martínez‐Urbistondo, Seyed‐Mohammad Fereshtehnejad, Imran Ali, Shinya Kimura, A. Wallace Hayes, Wenju Cai, Sabu Thomas, Kazem Rahimi, Armin Sorooshian, Michael Schreiber, Koichi Kato, John H. T. Luong, Stefano Pluchino, Andrés M. Lozano, John F. Seymour, Kenneth S. Kosik, Stefan G. Hofmann, Roger S. McIntyre, Matjaž Perc, Alexander Leemans, Robyn S. Klein, Shuji Ogino, Christopher Wlezien, George Perry, Juan J. Nieto, Lisa A. Levin, Daniel J. Klionsky, Bahram Mobasher, T. Dorigo, Nima Rezaei

Bibliographic record

VenueAdvances in experimental medicine and biology · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity Health NetworkUniversity of TorontoUniversity of OttawaToronto Western HospitalUniversity of ManitobaKrembil FoundationMcGill University
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical Sciences
KeywordsPandemicCoronavirus disease 2019 (COVID-19)GlobeMultidisciplinary approachGlobal health2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Socioeconomic statusFace (sociological concept)Economic growthHealth carePolitical scienceGeographyDevelopment economicsMedicineEnvironmental healthDiseaseInfectious disease (medical specialty)VirologySocial scienceSociologyEconomicsOutbreak

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.012
metaresearch head score (Gemma)0.015
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: Review · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.010
Scholarly communication0.0070.011
Open science0.0010.007
Research integrity0.0060.014
Insufficient payload (model declined to judge)0.0180.002

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 head0.255
GPT teacher head0.542
Teacher spread0.287 · 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
GenreReview

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

Citations4
Published2024
Admission routes1
Has abstractno

Explore more

Same venueAdvances in experimental medicine and biology→Same topicCOVID-19 and Mental Health→French-language works237,207→