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Record W3019624777 · doi:10.1093/schbul/sbaa051

The COVID-19 Global Pandemic: Implications for People With Schizophrenia and Related Disorders

2020· article· en· W3019624777 on OpenAlexafffund
Nicole Kozloff, Benoit H. Mulsant, Vicky Stergiopoulos, Aristotle N. Voineskos

Bibliographic record

VenueSchizophrenia Bulletin · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchUniversity of TorontoNational Institutes of HealthCentre for Addiction and Mental HealthNatural Sciences and Engineering Research Council of CanadaCentre for Addiction and Mental Health FoundationPfizerFondation Brain CanadaPatient-Centered Outcomes Research InstituteEli Lilly and Company
KeywordsPandemicSchizophrenia (object-oriented programming)PsychiatryCoronavirus disease 2019 (COVID-19)Mental healthPublic healthPsychologyMedicineDiseaseNursingInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The coronavirus disease-19 (COVID-19) global pandemic has already had an unprecedented impact on populations around the world, and is anticipated to have a disproportionate burden on people with schizophrenia and related disorders. We discuss the implications of the COVID-19 global pandemic with respect to: (1) increased risk of infection and poor outcomes among people with schizophrenia, (2) anticipated adverse mental health consequences for people with schizophrenia, (3) considerations for mental health service delivery in inpatient and outpatient settings, and (4) potential impact on clinical research in schizophrenia. Recommendations emphasize rapid implementation of measures to both decrease the risk of COVID-19 transmission and maintain continuity of clinical care and research to preserve safety of both people with schizophrenia and the public.

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)0.004
metaresearch head score (Gemma)0.013
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: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0150.002

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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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
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

Citations388
Published2020
Admission routes2
Has abstractyes

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