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Record W4413313919 · doi:10.1371/journal.pone.0330442

Changes in emotional distress among Ontario education workers during the COVID-19 pandemic: 2021–2023

2025· article· en· W4413313919 on OpenAlexafffundabout
Iris Gutmanis, Brenda L. Coleman, Kelly Ramsay, Robert Maunder, Susan J. Bondy, Kailey Fischer, Veronica Zhu, Allison McGeer

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsYork UniversityUniversity of TorontoSinai Health System
FundersCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of Canada
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Emotional distressDistressMedicineCoronavirus InfectionsPsychologyClinical psychologyEnvironmental healthPsychiatryVirologyAnxietyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Education workers experienced increased stress during the COVID-19 pandemic adapting to changing work locations, workload, and pedagogical approaches as well as dealing with pandemic-induced personal life stress. METHODS: The goal of this Canadian prospective cohort study was to determine whether levels of distress, as measured by the Kessler Psychological Distress Scale (K10), varied significantly over the course of the study (February 18, 2021 to December 22, 2023) among Ontario education workers after adjusting for demographic, work-related, and temporal factors. Exposure data were collected at enrollment and updated annually while vaccination and illness surveys were completed as needed. The K10 was completed periodically throughout the study. Linear mixed effects models were used to assess factors associated with changes in K10 scores. RESULTS: On average, K10 scores fell 0.5% every four weeks over the 34-month long study. However, the mean score, that decreased from 23.1 at study start to 19.3 by study end, remained above the cut-off of 15, indicating no ongoing emotional distress. Lower distress scores were also associated with non-winter seasons, older age, being male, and being in very good/excellent health. Alternatively, higher K10 scores were associated with being on medications to reduce anxiety, depression, or sleeping problems, return-to-workplace periods after school lockdowns, and periods of intense non-pharmacological interventions. CONCLUSION: Early identification of people most likely to experience distress is needed so that stress remediation strategies can be quickly implemented. Younger, female education workers with lower rated subjective health, who are taking medications to reduce anxiety, depression, or sleeping problems are likely to be at the highest risk. Recognizing periods that intensify stressful situations, such as pandemics and intervals within them, is important to anticipate the need for assistance.

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)0.001
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 score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.110
GPT teacher head0.379
Teacher spread0.270 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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