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Results of the COVID-19 mental health international for the general population (COMET-G) study
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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.516
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itQuick stats
Citations138
Published2021
Admission routes1
Has abstractno