Cancer Patients’ Experiences with Telehealth before and during the COVID-19 Pandemic in British Columbia
Bibliographic record
Abstract
BACKGROUND: Patients have had their cancer care either postponed or changed to telehealth visits to reduce exposure to COVID-19. However, it is unclear how these changes may have affected their experiences. We aim to identify patient characteristics that affect telehealth experiences and evaluate their preferences for using telehealth in the future. METHODS: Patients who completed the Outpatient Cancer Care (OCC) Patient Experience Survey were invited to participate. They comepleted the modified OCC Survey, which focused on telehealth during the pandemic. Linear and logistic regression analyses were used to identify patient characteristics that influenced telehealth experiences and preferences for future telehealth use. RESULTS: Perceived ease of participation in telehealth is a significant predictor of the change in patients' ratings of their telehealth experience. We found that cancer patients had lower preferences for using telehealth in the future if they were older, female, or non-white; resided in an urban area; had no previous telehealth experience; had lower education; and had poorer mental health. CONCLUSIONS: To optimize cancer care and improve equitable access to high-quality telehealth care during the pandemic and beyond, clinicians and policymakers will need to consider patients' self-reported experiences and their personal characteristics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".