Technology use in long-term care during the COVID-19 pandemic: A qualitative study of paid employees’ experiences in Western Canada
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, governments across the world implemented processes and policies to limit the spread of COVID-19, especially in long-term care (LTC) homes. This led to changes in technology use for persons living in LTC homes, their families and friends, as well as the paid workforce dedicated to caring for them. Objective: The study describes the role of technology and its impact on the experiences of LTC staff working in northern and rural areas in Western Canada during COVID-19. Methods: A secondary analysis of semi-structured interviews with 52 LTC staff was conducted. Qualitative data was analysed thematically using Braun and Clarke's thematic analysis approach. Results: Analysis of the study data revealed that new and innovative uses of technology emerged in the LTC setting during COVID-19, including technologies to support communication and collaboration with medical and health care professionals external to the LTC homes. Video-conferencing technology were rapidly implemented to facilitate virtual visits for LTC residents to connect to their families, further new streaming services were introduced to support recreational activities, including live music and spiritual services. LTC residents required significant support from staff to participate in virtual activities. Inadequate Internet infrastructure and scheduling difficulties in the context of severe staff shortages created challenges in technology adoption. Conclusions: This research provides insight into how technology can support LTC teams in northern and rural communities, as well as supports needed for LTC residents and staff to integrate technology effectively. The study informs actionable insights for those working in rural LTC settings.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".