Navigating the Virtual Landscape: Methodological Considerations for Qualitative Research in Long-Term Care
Bibliographic record
Abstract
With the COVID-19 pandemic halting all in-person research in March 2020, many researchers adopted virtual methods to continue their work amid this global crisis. As the pandemic persisted and the safety of participants and researchers remained a priority, virtual research grew in popularity for qualitative researchers. This in turn led to methodological insights on the application and advantages of conducting qualitative research using virtual methods. Virtual methods have been found to enhance participant comfort, facilitate open discussion of sensitive topics, alleviate fatigue in participants and researchers, and result in more engaging and focused interviews. While the body of evidence supporting virtual methods of data collection for nursing and other healthcare disciplines continues to grow, its application in the long-term care (LTC) setting remains underreported. In this paper, we discuss the virtual methods that we developed and implemented to successfully conduct a virtual qualitative single case study in a Canadian LTC home during the COVID-19 pandemic. Considerations from existing literature on virtual methods are discussed in parallel with strategies we implemented to successfully conduct a virtual study in LTC. This paper contributes to the growing body of literature on methodological insights into conducting virtual qualitative research in LTC. We provide evidence-based strategies for the virtual recruitment of study sites, study participants including residents, team members and families, and virtual data collection methods. These recommendations offer insights to overcome challenges and maximize the advantages of virtual methods, to enhance the quality and rigour of virtual qualitative research conducted within 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 | 0.680 | 0.655 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.020 | 0.039 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".