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Record W4280601496 · doi:10.1177/10398562221100090

Rapid review and commentary on the clinical implications of the population mental health consequences of the COVID-19 pandemic in Australia

2022· review· en· W4280601496 on OpenAlexaff
Jeffrey CL Looi, Stephen Allison, Tarun Bastiampillai, Steve Kisely

Bibliographic record

VenueAustralasian Psychiatry · 2022
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Mental health2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PopulationPsychologyPsychiatryMedicineEnvironmental healthVirologyOutbreakDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide a rapid clinical review and commentary for psychiatrists on the population mental health consequences of the COVID-19 pandemic in Australia, including evidence-based findings and interventions. CONCLUSIONS: Whilst there was evidence of collective psychological resilience during the first 2 years of the COVID-19 pandemic, younger women, carers for those with COVID-19, and those with more household chores, childcare needs and higher economic strain, were at more risk. Interventions should therefore target people with these socio-demographic risk factors, as well as severe COVID-19 survivors, their relatives and frontline workers. However, the rapid spread of the Omicron SARS-CoV-2 variant has the potential for greater impacts on population mental health. Innovations in telehealth and online therapy should be incorporated into standard care. Ongoing research is needed to assess who remains most vulnerable to negative mental health impacts of the current pandemic, and especially the longer term outcomes of mental ill health. Further research should also investigate evidence-based approaches to resilience and well-being. Prospective risk/benefit analyses of infection control measures, economic effects and mental health consequences are needed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.556
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.329
GPT teacher head0.543
Teacher spread0.214 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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