MétaCan
Menu
Back to cohort

Results of the COVID-19 mental health international for the general population (COMET-G) study

2021· article· en· W3206082612 on OpenAlexaff
Konstantinos Ν. Fountoulakis, Grigorios N. Karakatsoulis, Seri Abraham, Kristina Adorjan, Helal Uddin Ahmed, Renato D. Alarcón, Kiyomi Arai, Sani Salihu Auwal, Michael Berk, Sarah Bjedov, Julio Bobes, Teresa Bobes-Bascarán, Julie Bourgin-Duchesnay, Cristina Bredicean, Laurynas Bukelskis, Akaki Burkadze, Indira Indiana Cabrera Abud, Ruby Castilla‐Puentes, Marcelo Cetkovich, Héctor Colón-Rivera, Ricardo Corral, Carla Cortez-Vergara, Piirika Crepin, Domenico De Berardis, Sergio Zamora Delgado, David Freitas de Lucena, Avinash De Sousa, Ramona Di Stefano, Seetal Dodd, Livia Priyanka Elek, Anna Elissa, B. Erdelyi-Hamza, Gamze Erzın, Martín Etchevers, Peter Falkai, Adriana Farcas, I. А. Fedotov, Viktoriia Filatova, Nikolaos K. Fountoulakis, Iryna Frankova, Francesco Franza, Pedro Frias, Tatiana Galako, Cristián Javier Garay, Leticia García-Álvarez, María Paz García‐Portilla, Xénia Gonda, Tomasz Gondek, Daniela Morera González, Hilary Gould, Paolo Grandinetti, Arturo Grau, Violeta Groudeva, Michal Hagin, Takayuki Harada, M. Tasdik Hasan, Nurul Azreen Hashim, Jan Hilbig, Sahadat Hossain, Rossitza Iakimova, Mona Ibrahim, Felicia Iftene, Yulia Ignatenko, Matías Irarrázaval, Zaliha Ismail, Jamila Ismayilova, Asaf Jacobs, Miro Jakovljević, Nenad Jakšić, Afzal Javed, Helin Yılmaz Kafalı, Sagar Karia, Olga Kazakova, Doaa Khalifa, Олена Хаустова, Steve Koh, Svetlana Kopishinskaia, Korneliia Kosenko, Sotirios A. Koupidis, Illés Kovács, Barbara Kulig, Alisha Lalljee, Justine Liewig, Abdul Majid, Evgeniia Malashonkova, Khamelia Malik, Najma Iqbal Malik, Gulay Mammadzada, Bilvesh Mandalia, Donatella Marazziti, Darko Marčinko, Stephanie Martinez, Eimantas Matiekus, Gabriela Mejia, Roha Saeed Memon, Xarah Elenne Meza Martínez, Dalia Mickevičiūtė, Roumen Milev, Muftau Mohammed, Alejandro Molina-López, Petr Morozov, Nuru Suleiman Muhammad, Filip Mustač, Mika S. Naor, Amira Nassieb, Alvydas Navickas, Tarek Okasha, Liliya Panteleeva, Ion Papavă, Mikaella E. Patsali, Alexey Pavlichenko, Bojana Pejušković, Mariana Pinto da Costa, Mikhail Popkov, Dina Popović, Nor Jannah Nasution Raduan, Francisca Vargas Ramírez, Elmārs Rancāns, Salmi Razali, Federico Rebok, Anna Rewekant, Elena Flores, María Teresa Rivera-Encinas, Pilar A. Sáiz, Manuel Sánchez de Carmona, David Saucedo Martínez, Jo Anne Saw, Görkem Saygılı, Patricia Schneidereit, Bhumika Shah, Tomohiro Shirasaka, Ketevan Silagadze, Satti Sitanggang, Oleg Skugarevsky, Anna Spikina, Sridevi Sira Mahalingappa, Maria Stoyanova, Anna Szczegielniak, Simona Claudia Tamasan, Giuseppe Tavormina, Maurilio Giuseppe Maria Tavormina, Pavlos N. Theodorakis, Mauricio Tohen, Eva-Maria Tsapakis, Dina Tukhvatullina, Irfan Ullah, Ratnaraj Vaidya, Johann M. Vega‐Dienstmaier, Jeļena Vrubļevska, Olivera Vuković, O. Vysotska, Anna Yashikhina, Panagiotis Prezerakos, Daria Smirnova

Bibliographic record

VenueEuropean Neuropsychopharmacology · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsQueen's UniversityArtificial Intelligence in Medicine (Canada)Child, Adolescent and Family Mental Health
FundersWorld Health Organization
KeywordsMental healthDepression (economics)AnxietyPsychiatryDistressPopulationPsychological interventionClinical psychologyMental distressPsychologyMedicineDemographyEnvironmental health

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.109
GPT teacher head0.476
Teacher spread0.367 · 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

Citations138
Published2021
Admission routes1
Has abstractno

Explore more

Same venueEuropean NeuropsychopharmacologySame topicCOVID-19 and Mental HealthFrench-language works237,207