The Impact of COVID-19 Pandemic on the Mental Health of Frontline Workers of Toronto Homeless Shelters (CSWs)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".