Understanding patient experiences before and during the COVID-19 pandemic: A quasi-experimental comparison of in-person and virtual cancer care
Bibliographic record
Abstract
The COVID-19 pandemic prompted the immediate widespread implementation of virtual care appointments in Cancer Care Alberta (CCA). This study aimed to compare patient experiences and satisfaction with in-person care provided prior to the pandemic and virtual care provided after the COVID-19 outbreak. Surveys were conducted to compare patient satisfaction, using the Your Voice Matters (YVM) experience survey, between patients in the pre-pandemic in-person (baseline) and post-outbreak (virtual) cohorts. Generalized Linear Models (GLMs) with an ordinal logistic link were used, adjusting for self-reported health status and other covariates, to investigate the association between cohort type and patient satisfaction. Despite having higher overall health status, the virtual cohort reported statistically significantly lower satisfaction than the baseline with emotional concerns, referrals and resources, and friend/family involvement in their care. Patients in the virtual cohort were much less likely to have completed a routinely used symptom-based Patient Reported Outcomes (PROs) questionnaire, which may help explain satisfaction differences. The additional stressors brought about by the pandemic, as well as the mode of virtual care delivery, both likely contributed to the lower satisfaction of the virtual cohort as well. Understanding the key differences in experience between the two cohorts will inform the development of a larger virtual care strategy within CCA in the future. Experience Framework This article is associated with the Innovation & Technology lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".