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Record W4405318243 · doi:10.1093/milmed/usae398

A Brief Report on Sex-Specific Differences in Persistent Depression, Anxiety, and Loneliness Among Canadian Veterans During the COVID-19 Pandemic

2024· article· en· W4405318243 on OpenAlexafffundabout
Colleen Webber, Alyson Mahar, Kate St. Cyr, Heidi Cramm, Christina Reppas‐Rindlisbacher, Shailee Siddhpuria, Julie Hallet, Paula A. Rochon, Nicola T. Fear

Bibliographic record

VenueMilitary Medicine · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsQueen's UniversityWomen's College HospitalUniversity of British ColumbiaPublic Health OntarioUniversity of TorontoOttawa Hospital
FundersCanadian Institute for Military and Veteran Health ResearchCanadian Geriatrics Society
KeywordsLonelinessMedicineAnxietyMental healthDepression (economics)Public healthPsychiatryVeterans AffairsPandemicGerontologyCoronavirus disease 2019 (COVID-19)DiseaseInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

INTRODUCTION: Older adults are at increased risk of severe illness and mortality from Coronavirus disease of 2019 (COVID-19) infection. However, public health strategies aimed at reducing spread of COVID-19 may have resulted in increased mental health symptoms, particularly among older adults. Currently, little is known about whether older Veterans were more likely to experience persistent mental health symptoms during the COVID-19 pandemic than non-Veterans. The objectives of the current study were to (1) compare differences in persistent symptoms of anxiety, depression, and loneliness among a sample of Canadian Armed Forces Veterans and non-Veterans ≥55 years of age and (2) to evaluate potential sex-specific differences in persistent mental health symptoms. MATERIAL AND METHODS: The data for this study are drawn from a longitudinal survey of Canadian adults (55 years and older) during the COVID-19 pandemic. Ethical approval was received from the Women's College Hospital Research Ethics Board. Participants completed a baseline survey of sociodemographic, mental health-related, and COVID-19-related variables in May 2020 and 8 follow-up surveys monthly between May 2020 and January 2021. Modified Poisson regression models with robust standard errors were used to estimate risk of persistent symptoms of anxiety, depression, and loneliness. RESULTS: Eight hundred twenty-nine participants (13.7% [n = 114] Veterans) were included in the analysis of persistent depressive symptoms, 859 participants (14.0% [n = 120] Veterans) were included in the analysis of persistent anxiety symptoms, and 862 (13.9% [n = 120] Veterans) were included in the analysis of persistent symptoms of loneliness. When comparing male Veterans and non-Veterans, there were small but statistically insignificant differences in persistent symptoms of anxiety (adjusted relative risk [aRR], 0.59; 95% confidence interval [CI], 0.24-1.46), depression (aRR, 1.54; 95% CI, 0.63-3.77), or loneliness (aRR, 0.79; 95% CI, 0.36-1.75); similar small but statistically insignificant differences were observed in persistent symptoms of anxiety (aRR, 1.26; 95% CI, 0.51-3.09), depression (aRR, 1.16; 95% CI, 0.49-2.73), and loneliness (aRR, 1.33; 95% CI, 0.61-2.90) when comparing female Veterans to female non-Veterans. CONCLUSIONS: Qualitative, but statistically nonsignificant sex-specific differences in persistent symptoms of anxiety, depression, and loneliness during the early months of the COVID-19 pandemic were observed in this study comparing Veterans and non-Veterans. Additional sex-stratified analyses using larger samples or qualitative interviews may be useful in understanding the unique mental health experiences of older men, women, and gender diverse Veterans during the COVID-19 pandemic.

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)0.001
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 score0.131
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.084
GPT teacher head0.367
Teacher spread0.283 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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