Understanding the Experiences of Frontline Workers in Long-Term Care During the COVID-19 Pandemic: A Narrative Study
Bibliographic record
Abstract
In Canada, the COVID-19 pandemic has had devastating effects on the residents and staff in long-term care (LTC) facilities, yet little is known about the experiences of frontline workers in LTC facilities with infectious outbreaks. The need for specialized knowledge of experiential and subjective data is significant to help us improve our understanding of the effects of the pandemic on frontline workers, and to develop stronger practices and disaster preparedness in LTC settings. The purpose of this research was to gain a deeper understanding of the experience of frontline workers caring for residents in LTC homes during the COVID-19 outbreaks using narrative inquiry. Methods of data collection included interviews, photovoice, field notes, and a researcher’s journal. Participants captured photographs representing their experience working during the COVID-19 outbreak at their LTC home. Participants’ stories were collected in interviews through reflection on each of their photographs. Data analysis followed Frank’s hermeneutic method of analysis of stories. Data was explored for analytic interests that resonate through the multiple narratives; these included: psychosocial effects, support, loss of normalcy, increased workload, and altruism and dedication. This study demonstrates the importance of equipping frontline workers with relevant knowledge to feel prepared and confident in protecting the residents, themselves, and their families. It highlighted the importance of protecting the mental health of frontline workers and the influence support from management personnel has on the experience of frontline workers during infectious outbreaks. This study demonstrates the role that disaster preparedness and policy development can have in the wake of infectious outbreaks. This study gave the frontline workers in LTC homes an avenue to share their experiences and provide firsthand insight into the events that took place during the COVID-19 outbreaks in LTC homes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".