Virtual Cancer Care Equity in Canada: Lessons From COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic radically shifted healthcare delivery to patients with cancer. Virtual cancer care, or the remote delivery of health care, has become an important resource for patients in Canada to maintain access to cancer care during the pandemic. With an increased number of people regularly accessing the internet and smartphones being ubiquitous for nearly all ages, technology in health care has grown. Virtual cancer care has been referenced as the fourth pillar of cancer care and it appears it may be here to stay. This article explores the benefits and challenges associated with virtual cancer care and outlines the importance of ensuring it is safe and equitable. Oncology nurses can identify where virtual care can be used to mitigate inequities and call attention when these tools exacerbate inequities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".