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Record W4407296579 · doi:10.24095/hpcdp.45.2.04

Ontario healthcare workers who sought treatment for their mental health during the first five waves of the COVID-19 pandemic: a snapshot of self-referrals across the province

2025· article· en· W4407296579 on OpenAlexafffundvenueabout
Judith M. Laposa, Duncan H. Cameron, Kim Corace, Heather L. Bullock, Lauren Flavelle, Natalie Quick, Karen Rowa, Sara de la Salle, Katherin Creighton-Taylor, Stephanie Carter, Paul Kurdyak, Vanessa Saldanha, Randi E. McCabe

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMcMaster UniversityInstitute for Clinical Evaluative SciencesImpactRoyal Ottawa Mental Health CentreWaypoint Centre for Mental Health CareUniversity of OttawaPublic Health OntarioOntario Shores Centre for Mental Health SciencesSt. Joseph’s Healthcare HamiltonUniversity of TorontoCentre for Addiction and Mental Health
FundersGovernment of Ontario
KeywordsMental healthPandemicMedicineAnxietyHealth careWorryPsychiatryReferralDepression (economics)Family medicineCoronavirus disease 2019 (COVID-19)DiseaseInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Healthcare workers (HCWs) have reported COVID-19 pandemic-related adverse mental health impacts. We examined the demographic profile of HCWs who self-referred for mental health treatment, how referrals changed over time in relation to waves of COVID-19, what the main problem was for which HCWs sought treatment, and how this changed during the pandemic. METHODS: Five major healthcare institutions provided mental health supports to HCWs across Ontario during the pandemic. Data from May 2020 to March 2022 were collected from 2725 HCW self-referrals regarding referral frequency, main presenting mental health problem and demographic information including ethnicity, gender, age, healthcare setting, profession and whether the HCW had a prior mental health diagnosis or had received prior mental health treatment. RESULTS: Treatment-seeking HCWs who self-referred predominantly self-identified as female and White. Almost half were nurses, and almost half had received previous mental health treatment; a slightly higher percentage reported a prior mental health diagnosis. Over 60% of the overall sample of HCWs worked in hospitals. The timing of increases and decreases in monthly new referrals roughly aligned with the onset and ending, respectively, of COVID-19 waves. The top five most common presenting problems for treatment-seeking were generalized anxiety/worry symptoms, depression, situational crisis/acute stress response, difficulty with stress/occupational or financial, and posttraumatic stress symptoms. CONCLUSION: Ontario HCWs self-referred to access mental health supports during the COVID-19 pandemic. The majority sought treatment for generalized anxiety/worry or depression symptoms. Results of this study may inform system planning for future pandemics, as well as for HCW wellness programs for continued workplace stress in the postpandemic period.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.069
GPT teacher head0.413
Teacher spread0.344 · 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

Citations1
Published2025
Admission routes4
Has abstractyes

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