Vulnerability: An Interpretive Descriptive Study of Personal Support Workers’ Experiences of Working During the COVID-19 Pandemic in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Personal support workers (PSWs) are important healthcare workers providing essential services to thousands of Canadians. PSWs face many challenges that were exacerbated in the context of the COVID-19 pandemic. In this study we explore experiences of PSWs working through the pandemic in Ontario long-term care (LTC) homes by focusing on the vulnerability of such workers. METHODS: An interpretive description approach was adopted. Eleven PSWs working in LTC homes in an urban center in Ontario participated in semi-structured interviews between January and May 2022. Thematic analysis of the transcripts was informed by concepts of vulnerability. RESULTS: The results suggest that PSWs experienced inherent, situational, and pathogenic vulnerability. Inherent vulnerability was experienced in relation to risks of contracting the coronavirus working in person with residents, and of experiencing physical and psychological distress in relation to challenging interactions with staff, residents and their superiors. Situational vulnerability was experienced in relation to demanding workloads, which were intensified in the context of the pandemic. Participants expressed feeling undervalued, unappreciated, and disrespected, reflecting experiences of pathogenic vulnerability. The narratives shared by PSWs highlighted how the COVID-19 pandemic added new, and magnified pre-existing, challenges and vulnerability, affecting their health and well-being. CONCLUSIONS: Understanding risks faced by PSWs in LTC settings is crucial for developing targeted interventions and policies to support PSWs' health and well-being, mitigate factors that contribute to their vulnerability and promote the long-term sustainability of this caregiving workforce, ultimately enhancing the quality of care provided to residents in LTC facilities.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".