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Record W4409388022 · doi:10.1177/21582440251329974

Occupational Experiences of Public Safety Communicators During the COVID-19 Pandemic: A Qualitative Study

2025· article· en· W4409388022 on OpenAlexafffundabout
Emily Howe, Stephen Czarnuch, Rosemary Ricciardelli

Bibliographic record

VenueSAGE Open · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsMemorial University of Newfoundland
FundersMitacs
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakQualitative researchSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public relationsPublic healthPolitical sciencePsychologySociologyMedicineVirologyNursingSocial scienceOutbreak

Abstract

fetched live from OpenAlex

The COVID-19 pandemic created an unprecedented global crisis as the virus affected many, and lives were restricted by public health measures. Public Safety Communicators (PSCs; e.g., 9-1-1 operators, call-takers, dispatchers) faced unique challenges in their work as the pandemic progressed with shifting workloads as well as requirements to continue to work and to work in-person. Moreover, PSCs were the person to call when there was a medical or public safety emergency during the crisis of the COVID-19 pandemic. Given extant literature already suggesting PSCs have a high prevalence of mental health disorders, we conducted an online survey of PSCs ( n = 333) in Canada striving to interpret the nuance in PSCs experiences, with a focus on the effects of the COVID-19 pandemic. Responses to open-ended items were coded into four areas impacted: self-reported increase in stress, specific operational stresses and organizational stresses experienced by PSC during COVID-19, and the COVID-19 precautions implemented in communicator workplaces. Lessons learned from PSC experiences can be harnessed to better support essential crisis responses while maintaining and supporting employee wellness.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.273
GPT teacher head0.570
Teacher spread0.297 · 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.

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 routes3
Has abstractyes

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