Virtual Cancer Care During the COVID-19 Pandemic in Alberta: Evidence From a Mixed Methods Evaluation and Key Learnings
Bibliographic record
Abstract
PURPOSE: This study reports on a mixed methods evaluation conducted within a provincial cancer program in Alberta, Canada. The purpose was to capture key learnings from a rapid virtual care implementation because of the COVID-19 pandemic and to understand the impact on patient and staff experiences. METHODS: Administrative data were collected for 21,362 patients who had at least one virtual or in-person visit to any provincial cancer center from April 1, 2020, to June 10, 2020. Patient surveys were conducted with 397 randomly selected patients who had received a virtual visit. Surveys were also conducted with 396 Cancer Care Alberta staff. RESULTS: 14,906 virtual visits took place in this period, and about 40% of weekly visits were virtual. Significant differences were observed in both patient-reported symptom questionnaire completion rates and referrals to supportive care services between patients seen in-person and virtually. Patients receiving active treatments reported significantly lower levels of satisfaction with virtual visits than those seen for follow-up, but overall 90% of patients indicated interest in receiving virtual care in the future. Staff thought virtual visits increased patients' access to care but less than one third (31.5%) felt confident meeting patients' emotional needs and having conversations about disease progression and/or end of life virtually. CONCLUSION: The COVID-19 pandemic has driven the rapid implementation of virtual visits for cancer care delivery in health care settings. The findings from this mixed methods evaluation provide a concrete set of considerations for organizations looking to develop a large-scale, enduring virtual care strategy.
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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 | 0.004 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".