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Record W3094359083 · doi:10.14740/jmc3594

COVID-19 Pandemic and Uptake in Suicide Attempt Among Young People of Minority Population: A Case Series

2020· article· en· W3094359083 on OpenAlexvenueno aff
Chiedozie Ojimba, Terrence Tumenta, Amod Thanju, Kenneth Oforeh, Joy Osaji, Amal Saha, Leon Valbrun

Bibliographic record

VenueJournal of Medical Cases · 2020
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychosocialPandemicIsolation (microbiology)Depression (economics)PsychiatryAnxietyPopulationSocial isolationMental healthCoronavirus disease 2019 (COVID-19)DiseaseEnvironmental healthInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The first case of coronavirus disease 2019 (COVID-19) was reported in Wuhan China on December 31, 2019. COVID-19 was declared a global pandemic on March 11, 2020. To reduce the spread of this virus, the World Health Organization (WHO) and the Center for Disease Control (CDC) recommended self and mandatory quarantine of exposed individuals and self-isolation. However, the psychological impact of this pandemic includes new onset or worsening of existing mental illnesses which include but are not limited to anxiety, depression from social isolation, eating disorders, and uptake in suicidality either in isolation or part of mental illness symptomatology. In the USA, suicide is the second leading cause of death among people aged 10 - 34 years while globally, it is the second cause of death among people aged 15 - 29 years. The authors present a case of two young women of minority population with no prior psychiatric illnesses who presented to the psychiatry emergency room with suicidal attempts due to COVID-19 pandemic-related psychosocial stressors.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.003
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 head0.129
GPT teacher head0.436
Teacher spread0.307 · 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 designCase report
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

Citations4
Published2020
Admission routes1
Has abstractyes

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