COVID-19 Impact on Alberta Nursing Home Workers: An Interpretive Descriptive Study With Direct Care Providers
Bibliographic record
Abstract
Abstract COVID-19 has devastated the LTC sector, but we lack systematic information on the impact on frontline staff. Our research, a partnership with the continuing care branches of Alberta Health and Alberta Health Services, was aimed at assessing COVID-19 impacts on staffs’ well-being and quality of work-life and quality of care and life among residents. Here we report on staff. Using an interpretive descriptive approach, we interviewed 140 staff from January through April 2021, in 34 nursing homes. Facilities selected varied in ownership (public/private) and COVID-19 status (high, moderate, or low incidence). Virtual interviews focused on three key areas of impact: (a) staff mental and physical health, well-being, and work-life, (b) the facility, and (c) on residents. Interviews were analyzed using inductive content analysis. Dominant themes included a commitment of staff to resident wellbeing; a norm of stoicism in which accumulative stress of COVID-19 is recognized in participants’ private lives but not their work; the critical role of teamwork in managing extra workload associated with COVID-19 protocols; role flexibility, particularly managers’, enables workers to minimize interruptions to care activities; governmental wage subsidies and the restriction of workers to only one facility benefits residents and workers in terms of time and familiarity, but some health care aides faced a wage reduction of 30-40%. Alongside the research component, we regularly met with stakeholders and end-users to discuss emerging findings and potential areas needing urgent intervention, as well as longer-term programming as the impact of COVID-19 will persist for many years.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".