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Record W4311199974 · doi:10.1177/07067437221140375

Suicidal Ideation Amongst University Students During the COVID-19 Pandemic: Time Trends and Risk Factors

2022· article· en· W4311199974 on OpenAlexafffundvenueabout
Laura Jones, Melissa Vereschagin, Angel Y Wang, Richard J. Munthali, Julia Pei, Chris G. Richardson, Priyanka Halli, Hui Xie, Brian Rush, Lakshmi N. Yatham, Anne Gadermann, Krishna Pendakur, Ana Paula Prescivalli, Lonna Munro, Ronny Bruffaerts, Randy P. Auerbach, Philippe Mortier, Daniel Vigo

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of TorontoSimon Fraser UniversityUniversity of British Columbia
FundersHealth Canada
KeywordsSuicidal ideationMental healthDemographyAnxietyPsychologyClinical psychologyLogistic regressionPandemicPsychiatryPoison controlMedicineOdds ratioSuicide preventionCoronavirus disease 2019 (COVID-19)Environmental healthSociologyDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: Examine time trends in suicidal ideation in post-secondary students over the first three waves of the COVID-19 pandemic in Canada and identify subpopulations of students with increased risk. METHOD: We analysed 14 months of data collected through repeated cross-sectional deployment of the World Health Organization (WHO) World Mental Health-International College Student (WMH-ICS) survey at the University of British Columbia. Estimated log odds weekly trends of 30-day suicidal ideation (yes/no) were plotted against time with adjustments for demographics using binary logistic generalized additive model (GAM). Risk factors for 30-day suicidal ideation frequency (four categories) were examined using the ordered logistic GAM, with a cubic smoothing spline for modelling time trend in obervation weeks and accounting for demographics. RESULTS: Nearly one-fifth (18.9%) of students experienced suicidal ideation in the previous 30 days. While the estimated log odds suggested that binary suicidal ideation was relatively stable across the course of the pandemic, an initial drop followed by an increasing trend was observed. Risk factors for suicidal ideation frequency during the pandemic included identifying as Chinese or as another non-Indigenous ethnic minority; experiencing current symptoms of depression or anxiety; having a history of suicidal planning or attempts; and feeling overwhelmed but unable to get help as a result of COVID-19. Older age was identified as a protective factor. CONCLUSIONS: The general university student population in our study was relatively resilient with respect to suicidal ideation during the first three waves of the pandemic, but trends indicate the possibility of delayed impact. Specific sub-populations were found to be at increased risk and should be considered for targeted support. Further analyses should be undertaken to continue monitoring suicidality trends throughout the remainder of the pandemic and beyond.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.457

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.036
GPT teacher head0.337
Teacher spread0.301 · 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

Citations28
Published2022
Admission routes4
Has abstractyes

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