We did our Best!: The Experience of Frontline Workers in Long-Term Care during COVID-19 Outbreaks
Bibliographic record
Abstract
In Canada, the COVID-19 pandemic had devastating effects for those living in long-term care (LTC) homes, yet little is known about the experiences of the frontline workers who endured in those settings with COVID-19 outbreaks. Specialized knowledge will improve our understanding of the effects of the pandemic on frontline workers (FW), enabling the development of stronger practices. The purpose of this research was to gain a deeper understanding of the experiences of FW caring for residents in LTC homes during a COVID-19 outbreak, using narrative inquiry. The methods used for data collection include interviews, field notes, and photovoice. Participants were asked to capture photographs representing their experience working during the COVID-19 outbreak at their LTC home. Participants' stories were collected through reflection on their photographs in interviews. The setting for this research was LTC homes in Ontario, Canada. Data analysis followed Frank's hermeneutic method of analysis of stories. Psychosocial effects, support, loss of normalcy, increased workload, and altruism and dedication resonated throughout the three participants' stories. The burnout, stress, and mental exhaustion, as detailed by the participants, emphasizes the importance of protecting the mental health of the FW during outbreaks. Equipping FW with relevant knowledge helped them to feel prepared and confident in protecting the residents, themselves, and their families. Support from management personnel influences the experience of FW during infectious outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".