Canadian Cancer Centre Response to COVID-19 Pandemic: A National and Provincial Response
Bibliographic record
Abstract
BACKGROUND: COVID-19 has spread rapidly, requiring health delivery systems to undertake dramatic transformations. To evaluate these system changes, we undertook one of the first Canadian health delivery system reviews and the first Canadian cancer centre evaluation of pandemic system modifications. METHODS: Questionnaires were distributed to the Canadian Association of Provincial Cancer Agencies (CAPCA) members in order to assess changes to cancer centre services and patient management. Documentation relating to COVID-19 from the CAPCA electronic space was accessed, and all publicly available cancer centre documentation related to COVID-19 was reviewed. RESULTS: Seven provinces completed the questionnaire and had documentation available from the CAPCA electronic space. All screening programs across Canada were suspended. In most provinces surveyed, ≥50% of outpatient appointments were occurring virtually, with <25% using video platforms. Generally, the impact on diagnostic imaging and new patient referrals correlated with the impact of COVID-19. Most provinces had a reduction in operating room availability, with chemotherapy and radiation treatments continuing. Public health modification, including personal protective equipment and screening staff, varied across the country. CONCLUSION: Canadian cancer centres underwent a rapid and aggressive transformation of services in response to COVID-19, with many similarities and differences across provinces. In part, this response was facilitated by communication under a national association, which in Canada remains unique to cancer. This response may serve to inform changes in other jurisdictions or disease states now and in future waves of the pandemic, as well as a record of changes for future health services and patient outcome research.
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 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 | 0.013 |
| 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 | 0.000 |
| 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".