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Assessment of Patients' Referral Patterns with Complaints ofSelf-harm and Aggression in the COVID-19 Era

2023· article· en· W4376876400 on OpenAlexaff
Amirmasoud Kazemzadeh Houjaghan, Pantea Arya, Sepideh Aarabi, Haleh Ashraf, Maryam Bahreini

Bibliographic record

VenueCurrent Psychiatry Research and Reviews · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsPsychiatryIrritabilitySuicidal ideationMoodMedicineAggressionMental healthPandemicClinical psychologyPsychologyAnxietyPoison controlSuicide preventionCoronavirus disease 2019 (COVID-19)DiseaseMedical emergency

Abstract

fetched live from OpenAlex

Background: Due to the high transmission rate of COVID-19, the high prevalence of the disease, the high mortality rate, and its effects on mental health, we aimed to assess the current status of psychiatric symptoms. Methods: In this observational study, we have assessed various psychiatric presentations and disorders before and after the COVID-19 pandemic within the same time limit. Data have been obtained from the psychiatric interview performed by an attending physician in psychiatry. Results: The following features have been observed after the pandemic: increased depressed mood, irritability, crime trend, physical violations, personality disorders along with improved family support, and decreased suicidal ideation. No significant difference has been observed in the rate of response to psychotherapy and psychiatric medications before and after the time of the pandemic. Conclusion: Increased physical threat and aggression, substance use, and symptoms of psychosis were more frequently observed in the time of the pandemic. The physical threat was mainly committed by younger patients with psychiatric illnesses.

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 distilled prediction

Teacher imitation

Not 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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.338
GPT teacher head0.574
Teacher spread0.236 · 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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

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