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Record W4206649248 · doi:10.1007/s12671-021-01822-2

Compassion Protects Mental Health and Social Safeness During the COVID-19 Pandemic Across 21 Countries

2022· article· en· W4206649248 on OpenAlexafffund
Marcela Matos, Kirsten McEwan, Martin Kanovský, Júlia Halamová, Stanley R. Steindl, Nuno Ferreira, Mariana Linharelhos, Daniel Rijo, Kenichi Asano, Margarita G. Márquez, Sónia Gregório, Sara P. Vilas, Gonzalo Brito‐Pons, Paola Lucena‐Santos, Margareth da Silva Oliveira, Érika Leonardo de Souza, Lorena Llobenes, Natali Gumiy, Maria Ileana Costa, Noor Habib, Reham Hakem, Hussain Khrad, Ahmad Alzahrani, Simone Cheli, Nicola Petrocchi, Elli Tholouli, Philia Issari, Gregoris Simos, Vibeke Lunding‐Gregersen, Ask Elklit, Russell L. Kolts, Allison C. Kelly, Catherine Bortolon, Pascal Delamillieure, Marine Paucsik, Julia E. Wahl, Mariusz Zięba, Mateusz Zatorski, Tomasz Komendziński, Shuge Zhang, Jaskaran Basran, Antonios Kagialis, James N. Kirby, Paul Gilbert

Bibliographic record

VenueMindfulness · 2022
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Waterloo
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVAgentúra na Podporu Výskumu a VývojaConselho Nacional de Desenvolvimento Científico e TecnológicoFundação para a Ciência e a TecnologiaFederation for the Humanities and Social Sciences
KeywordsCoronavirus disease 2019 (COVID-19)PandemicCompassionPublic healthMental health2019-20 coronavirus outbreakPsychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MindfulnessPsychiatryPsychotherapistPolitical scienceMedicineVirologyNursingDiseaseInfectious 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)0.002
metaresearch head score (Gemma)0.005
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 score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.373
Teacher spread0.320 · 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

Citations80
Published2022
Admission routes2
Has abstractno

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