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Record W7010413927

The Impact of COVID-19 Pandemic on the Mental Health of Frontline Workers of Toronto Homeless Shelters (CSWs)

2025· article· en· W7010413927 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Crossing (Liberty University) · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPandemicContext (archaeology)ProductivityMental health serviceService (business)Grounded theoryParticipant observationJob satisfaction
DOInot available

Abstract

fetched live from OpenAlex

The objective of this phenomenological research is to understand the impact of COVID-19 on the mental health of the client service workers (CSWs) in Toronto homeless shelters. Kolcaba's theory of comfort guides this study. This theory holds that being comfortable is one of the fundamental necessities for relief after stressful health care events or incidents. Comfort boosts health-seeking behaviors for patients, clients, and workers. In the context of the City of Toronto’s homeless shelters, Kolcaba’s theory of comfort suggests that if client service workers are given adequate resources to balance their health and mental needs, thus stabilizing their comfortabilities, then their job satisfaction would improve quickly. This in turn would increase productivity and enhance customer service. The second theory guiding this study is Orem’s Care-deficit theory. This study involves eight participant CSWs, four men and four women, working in the city of Toronto homeless shelters. They answer 21 semi-structured interview questions. The study finds that strict measures like mandatory PPE, vaccination, social distancing, and constant sanitizing protocols instituted by the province and the city authorities at the onset of the COVID-19 pandemic disrupted the chance for normalcy at work, leaving vulnerable workers with fear, anxiety, depression, frustration, uncertainties, and anger.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.411
Teacher spread0.360 · 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 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 routes1
Has abstractyes

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