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Record W4409322803 · doi:10.1016/j.ssaho.2025.101385

Qualitative analysis of negative and positive COVID-19 experiences of frontline client educators in a Canadian maritime province

2025· article· en· W4409322803 on OpenAlexaffabout
Devesh Oberoi, Saraydarian Sandra, Janine Giese‐Davis

Bibliographic record

VenueSocial Sciences & Humanities Open · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsHatch (Canada)Dalhousie UniversityUniversity of Calgary
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakQualitative researchSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicPsychologyQualitative analysisMedicineSociologyVirologySocial scienceOutbreakInternal medicine

Abstract

fetched live from OpenAlex

The worldwide COVID-19 pandemic dramatically shifted the ways that citizens lived, increasing rates of mental-health concerns. Frontline-worker stress escalated due to health risks while exposing workplaces ill-equipped to adapt to crises. Educators struggling with occupational stress pre-pandemic were particularly hard hit, as were healthcare and mental-health clinicians and systems. In a Canadian maritime province, we launched a longitudinal proof-of-concept study of a virtual-mental-health intervention to improve quality of life for members of a teachers' union. A vital first question was to understand these frontline educators’ lived experiences during COVID-19 prior to receiving intervention. We selected 60 frontline educators (who were actively working) as study participants from our 9-month rolling consecutive enrollment. At baseline, prior to intervention, they completed an online (Qualtrics) survey including COVID-19 exposures, stressors, and open-ended narrative questions reporting their negative and positive experiences. We reported exposures and stressors and conducted thematic qualitative analysis of their negative and positive experience narratives. During this baseline study (June 2021–March 2022), 24 (of 60) educators reported COVID-19 diagnoses in themselves/friends/family, while 1 educator reported the death of a friend. In a pre-set list of COVID-19 stressors, those most endorsed (77–88%; slight to severe distress) included four issues: 1) inability to see family, 2) and friends, 3) working in a face-to-face environment, and 4) isolation. In qualitative analyses, negative COVID-19 experiences included social isolation, mental/physical health declines, work-life imbalance, loss of major-life-transition events, parenting/caregiver burden, and professional stressors. Positive experiences included slowing down and stepping back, shifting priorities, improved wellbeing, intentional connections, practical benefits, and growth and resiliency. Many reported that COVID-19 had an unforeseen “silver lining” allowing these workers to find solace. General-population cross-sectional and longitudinal papers discuss COVID-19 distress and increasingly report positive experiences. Our study differs by examining frontline educators distressed enough to seek mental-health intervention. We find that themes of distress and resilience provide insight into these workers’ experiences and point to ways institutions could foster resilience. • Frontline Educators reported both negative and positive Covid-19 experiences. • Negative: social isolation, mental/physical health declines, work-life imbalance, loss of major-life-transition events, parenting/caregiver burden, and professional stressors • Positive: slowing down and stepping back, shifting priorities, improved wellbeing, intentional connections, practical benefits, and growth and resiliency • Resilience: access to mental health services, pauses during workday for self-care.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.116
GPT teacher head0.530
Teacher spread0.414 · 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 designQualitative
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 routes2
Has abstractyes

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