Report from the Ready for the Next Round Thought-Leadership Roundtables on Building Resilience in Cancer Care and Control in Canada-Colorectal Cancer Canada; 2021
Bibliographic record
Abstract
(1) Background: The COVID-19 pandemic illuminated vulnerabilities in the Canadian health care system and exposed gaps and challenges across the cancer care continuum. Canada is experiencing significant disruptions to cancer-related services, and the impact these disruptions (delays/deferrals/cancellations) have on the health care system and patients are yet to be determined. Given the potential adverse ramifications, how can Canada's health care systems build resilience for future threats? (2) Methods: To answer this question, CCC facilitated a series of four thought-leadership roundtables, each representing the views of four different stakeholder groups: patients, physicians, health care system leaders, and researchers. (3) Results: Six themes of strength were identified to serve as a springboard for building resilience including, (1) advancing virtual care and digital health technologies to prevent future interruptions in cancer care delivery. (2) developing real-time data metrics, data sharing, and evidence-based decision-making. (3) enhancing public-private-non-profit partnerships to advance research and strengthen connections across the system. (4) advancing patient-centricity in cancer research to drive and encourage precision medicine approaches to care. (5) investing in training and hiring a robust supply of health care human resources. (6) implementing a national strategy and infrastructure to ensure inter-provincial collaborative data sharing (4). Conclusions: A resilient health care system that can respond to shocks and threats is not an emergency system; it is a robust everyday system that can respond to emergencies.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".