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Record W3141367304 · doi:10.1016/s2215-0366(21)00091-2

Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries

2021· article· en· W3141367304 on OpenAlexafffund
Jane Pirkis, Ann John, Sangsoo Shin, Marcos DelPozo‐Baños, Vikas Arya, Pablo Analuisa-Aguilar, Louis Appleby, Ella Arensman, Jason Bantjes, Anna Baran, José Manoel Bertolote, Guilherme Borges, Petrana Brečić, Eric D. Caine, Giulio Castelpietra, Shu‐Sen Chang, David Colchester, David Crompton, Marko Ćurković, E Deisenhammer, Chengan Du, Jeremy Dwyer, Annette Erlangsen, Jeremy Samuel Faust, Sarah Fortune, Andrew Garrett, Devin George, Rebekka Gerstner, Renske Gilissen, Madelyn S. Gould, Keith Hawton, Joseph Kanter, Navneet Kapur, Murad Moosa Khan, Olivia J Kirtley, Duleeka Knipe, Kairi Kõlves, Stuart Leske, Kedar Marahatta, Ellenor Mittendorfer‐Rutz, Н. Г. Незнанов, Thomas Niederkrotenthaler, Emma Nielsen, Merete Nordentoft, Herwig Oberlerchner, Rory C. O’Connor, Melissa Pearson, Michael R. Phillips, Steve Platt, Paul L. Plener, Georg Psota, Ping Qin, Daniel Radeloff, Christa Rados, Andreas Reif, Christine Reif-Leonhard, Vsevolod Rozanov, Christiane Schlang, Barbara Schneider, Н. В. Семенова, Mark Sinyor, Ellen Townsend, Michiko Ueda, Lakshmi Vijayakumar, Roger T. Webb, Manjula Weerasinghe, Gil Zalsman, David Gunnell, Matthew J. Spittal

Bibliographic record

VenueThe Lancet Psychiatry · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreHealth Sciences Centre
FundersNational Health and Medical Research CouncilPatient Safety Translational Research CentreNIHR Greater Manchester Patient Safety Translational Research CentreVlaamse regeringMedizinische Universität WienElizabeth Blackwell Institute for Health Research, University of BristolUniversity of TorontoFonds Wetenschappelijk OnderzoekNational Natural Science Foundation of ChinaMedical Research CouncilMental Health CommissionSwansea UniversityUniversity of ManchesterHealth Research BoardRoyal Perth Hospital Medical Research FoundationNational Institute for Health and Care ResearchAustralian Research CouncilU.S. Department of JusticeVienna Science and Technology FundAustralian GovernmentErasmus+MQ: Transforming Mental HealthNHS Health ScotlandWorld Health OrganizationWellcome TrustScottish GovernmentQueensland HealthUniversity of BristolInternational Association for Suicide PreventionUniversity Hospitals Bristol NHS Foundation TrustNational Coronial Information SystemAmerican Foundation for Suicide PreventionEuropean CommissionHealth and Care Research WalesUniversität Wien
KeywordsPandemicGovernment (linguistics)Public healthCoronavirus disease 2019 (COVID-19)Mental healthSuicide preventionPoison controlDemographyGeographyMedicineMedical emergencyPsychiatrySociology

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)0.002
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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.150
GPT teacher head0.440
Teacher spread0.290 · 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

Citations745
Published2021
Admission routes2
Has abstractno

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