“I would have to walk around to find the best Wi-Fi connection…”: qualitatively exploring challenges associated with rapid rollout of telehealth in Canadian long-term care homes
Bibliographic record
Abstract
Abstract Background Early in the COVID-19 pandemic, long-term care (LTC) homes in British Columbia, Canada, restricted visitation to ensure the safety of their residents against transmission of the novel coronavirus. As such, these LTC homes had to quickly implement a rapid rollout of telehealth services to maintain physician care for residents while avoiding the infection risk of in-person visits amidst lockdown measures. The abrupt transition from traditional in-person physician care to telehealth presented significant challenges. Investigating these challenges is pivotal to the development of strategies for sustained telehealth use for physician services in LTC homes. This analysis is part of a broader qualitative, utilization-focused evaluation study of telehealth services rapidly implemented for physician care in LTC homes within the Fraser Health Authority region of British Columbia. The evaluation has aimed to consider integral factors such as telehealth challenges, facilitators, preferences, and continued use. Semi-structured interviews and focus groups were conducted with 70 physicians, staff, residents, and family caregivers across 27 different LTC homes in the region. All interviews and focus groups were transcribed verbatim and were analyzed using a thematic approach to identify common barriers surrounding the rapid rollout of telehealth in LTC across relevant groups. Results From the data, four challenges were identified: connectivity challenges (e.g., inconsistent or no Wi-Fi or cellular connectivity), device challenges (e.g., lack of accessible devices and software issues), privacy challenges (e.g., lack of private space to support telehealth use), and informational challenges (e.g., lack of electronic medical record access). All challenges posed barriers to telehealth access for both care provider and recipient groups in LTC settings. Conclusions The challenges identified in this analysis are supported by existing literature, which is significant given the different contexts within which such research has been undertaken. Collectively, this knowledge base can support evidence-informed improvements to telehealth for physician care in LTC settings. Future research should capture the perspectives of diverse cultural groups, LTC residents with cognitive impairments, and those who provide and receive care in rural settings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".