Cracks in the foundation: The experience of care aides in long‐term care homes during the <scp>COVID</scp> ‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: Care aides (certified nursing assistants, personal support workers) are the largest workforce in long-term care (LTC) homes (nursing homes). They provide as much as 90% of direct care to residents. Their health and well-being directly affect both quality of care and quality of life for residents. The aim of this study was to understand the impact of COVID-19 on care aides working in LTC homes during the first year of the pandemic. METHODS: We conducted semi-structured interviews with a convenience sample of 52 care aides from 8 LTC homes in Alberta and one in British Columbia, Canada, between January and April 2021. Nursing homes were purposively selected across: (1) ownership model and (2) COVID impact (the rate of COVID infections reported from March to December 2020). Interviews were recorded and analyzed using inductive content analysis. RESULTS: Care aides were mainly female (94%) and older (74% aged 40 years or older). Most spoke English as an additional language (76%), 54% worked full-time in LTC homes, and 37% worked multiple positions before "one worksite policies" were implemented. Two themes emerged from our analysis: (1) Care aides experienced mental and emotional distress from enforcing resident isolation, grief related to resident deaths, fear of contracting and spreading COVID-19, increased workload combined with staffing shortages, and rapidly changing policies. (2) Care aides' resilience was supported by their strong relationships, faith and community, and capacity to maintain positive attitudes. CONCLUSIONS: These findings suggest significant, ongoing adverse effects for care aides in LTC homes from working through the COVID-19 pandemic. Our data demonstrate the considerable strength of this occupational group. Our results emphasize the urgent need to appropriately and meaningfully support care aides' mental health and well-being and adequately resource this workforce. We recommend improved policy guidelines and interventions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".