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Record W4313544955 · doi:10.1038/s41597-022-01896-0

Author Correction: COVIDiSTRESS diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic

2023· erratum· en· W4313544955 on OpenAlexaff
Angélique M. Blackburn, Sara Vestergren, Thao Tran, Sabrina Stöckli, Siobhán M. Griffin, Evangelos Ntontis, Alma Jeftić, Stavroula Chrona, Gözde İkizer, Hyemin Han, Taciano L. Milfont, Douglas A. Parry, Grace Byrne, Mercedes Gómez-López, Alida Acosta, Marta Kowal, Gabriel De Leon, Aranza Gallegos, Miles Perez, Mohamed Abdelrahman, Elayne Ahern, Ahmad Wali Ahmad Yar, Oli Ahmed, Nael H. Alami, Rizwana Amin, Lykke E. Andersen, Bráulio Oliveira Araújo, Norah Aziamin Asongu, Fabian Bartsch, Jozef Bavoľár, Khem Raj Bhatta, Tuba Bircan, Bita Shalani, Hasitha Bombuwala, Tymofii Brik, Hüseyin Çakal, Marjolein C.J. Caniëls, Marcela Carballo, Nathalia Melo de Carvalho, Laura Cely, Sophie Chang, María Chayinska, Fang-Yu Chen, Brendan Ch’ng, JohnBosco Chika Chukwuorji, Ana Raquel Costa, Vidijah Ligalaba Dalizu, Eliane Deschrijver, İlknur Dilekler Aldemir, Anne M. Doherty, Rianne Doller, Dmitrii Dubrov, Salem Elegbede, Jefferson Elizalde, Eda Ermağan Çağlar, Regina F. Fernandez, Juan Diego García‐Castro, Rebekah Gelpí, Shagofah Ghafori, Ximena Goldberg, Catalina González-Uribe, Harlen Alpízar-Rojas, Christian A. P. Haugestad, Diana Higuera, Kristof Hoorelbeke, Evgeniya Hristova, Barbora Hubená, Hamidul Huq, Keiko Ihaya, Gosith Jayathilake, Enyi Jen, Amaani Jinadasa, Jelena Joksimović, Pavol Kačmár, Veselina Kadreva, Kalina Nikolova Kalinova, Huda Anter Abdallah Kandeel, Blerina Këllezi, Sammyh S. Khan, Maria Kontogianni, Karolina Koszałkowska, Krzysztof Hanusz, David Lacko, Miguel Landa–Blanco, Yookyung Lee, Andreas Lieberoth, Samuel Lins, Liudmila Liutsko, Amanda Londero‐Santos, Anne Lundahl Mauritsen, María Andrée Maegli, Patience Magidie, Roji Maharjan, Tsvetelina Makaveeva, Malose Makhubela, María Gálvis Malagón, Sergey Malykh, Salomé Mamede, Samuel Mandillah, Mohammad Sabbir Mansoor, Silvia Mari, Inmaculada Marín‐López, Tiago Azevedo Marot, Sandra Martínez Pérez, Juma Mauka, Sigrun Marie Moss, Asia Mushtaq, Arian Musliu, Daniel Mususa, Arooj Najmussaqib, Aishath Nasheeda, Ramona Nasr, Natalia Niño, Jean Carlos Natividade, Honest Prosper Ngowi, Carolyne Nyarangi, Charles A. Ogunbode, Charles Onyutha, K. Padmakumar, Walter Paniagua, María Caridad Peña, Martin Pírko, Mayda Portela, Hamidreza Pouretemad, Nikolay R. Rachev, Muhamad Ratodi, Jason Reifler, Saeid Sadeghi, Harishanth Samuel Sahayanathan, Eva María Torrecilla Sánchez, Ella Marie Sandbakken, Sandesh Dhakal, Shrestha Sanjesh, Jana Schrötter, Sabarjah Shanthakumar, Pilleriin Sikka, Konstantina Slaveykova, Anna Studzińska, Fadelia Deby Subandi, Namita Subedi, Gavin Brent Sullivan, Benjamin Tag, Takem Ebangha Agbor Delphine, William Tamayo-Agudelo, Giovanni A. Travaglino, Jarno Tuominen, Tuğba Türk Kurtça, Vakai Matutu, Tatiana Volkodav, Austin Horng-En Wang Wang, Alphonsus Williams, Charles K. S. Wu, Yuki Yamada, Teodora Yaneva, Nicolás Yañez, Yao‐Yuan Yeh, Emina Zoletić

Bibliographic record

VenueScientific Data · 2023
Typeerratum
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicineVirologyOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The original version of this Article contained an error in the spelling of the author Krzysztof Hanusz, which was incorrectly given as Hanusz Krzysztof. This has now been corrected in both the PDF and HTML versions of the Article.

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)0.010
metaresearch head score (Gemma)0.162
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: Other · Consensus signal: none
Teacher disagreement score0.116
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.162
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.009
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.1160.076

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 head0.452
GPT teacher head0.521
Teacher spread0.068 · 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
GenreOther

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

Citations1
Published2023
Admission routes1
Has abstractyes

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