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Record W4392607873 · doi:10.2139/ssrn.4745065

Global Perspectives of Nurses from 37 Countries About Pandemic Response Implementation between 2021 and 2023

2024· preprint· en· W4392607873 on OpenAlexaff
Allison Squires, Hillary Dutton, Larissa Burka, Guadalupe Casales Hernandez, Raymond Aborigo, Samuel Byiringiro, Ho Yu Cheng, Shanzida Katun, Tae Wha Lee, Iwona Malinowska Lipien, Alvisa Palese, Verónica Beatriz Sánchez Ramírez, Juliana Smichenko, Anna Zisberg, Fadumo Osman Ahmed, José Luis Álvarez Watson, Theresa Amaya, Maria Anyorikeya, Maryuri Ibeth Arteaga Cordova, Cornelia Bernal Cespedes, Aurelija Blasevenice, Lilia Buitrago, Hülya Bilgin, Gabriella Castillo, Theresa P. Castillo, Stefanía Johanna Cedeño Tapia, Stefania Chiappinotto, Dulamsuren Daimiran, Blerina Duka, Vlora Ejupi, Naxhije Fetai, Yesenia Flores, Julio Bobes, Cibeles Gonzalez, Abdullah Gruda, José Luís Guedes dos Santos, Mahamed Jamac Ismai, Juana Jiménez Sánchez, Seung Eun Lee, Messias Lemos, Jakub Lickiewicz, Jannette Marga Loza Sosa, Jūratė Macijauskienė, Derby Muñoz Rojas, Abdulqadir J. Nashwan, Aylin Özakgül, Eda Özkara Şan, Taycia Ramirez, Javier Isidro Rodríguez López, Paola Saldariagga, Halyna Skipalska, Zeliha Tülek, Alisa Tsuladze, Maia Uchanieshvili, Enkhjargal Yanjmaa, Simon Jones, Philip Resnik

Bibliographic record

VenueSSRN Electronic Journal · 2024
Typepreprint
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsVictorian Order of Nurses
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Political scienceEconomic growthMedicineEconomicsInternal medicineInfectious disease (medical specialty)

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)0.010
metaresearch head score (Gemma)0.018
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0070.004
Open science0.0010.011
Research integrity0.0050.009
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 head0.029
GPT teacher head0.442
Teacher spread0.414 · 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

Citations0
Published2024
Admission routes1
Has abstractno

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