Silver linings: a qualitative study of desirable changes to cancer care during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Public health emergencies and crises such as the current COVID-19 pandemic can accelerate innovation and place renewed focus on the value of health interventions. Capturing important lessons learnt, both positive and negative, is vital. We aimed to document the perceived positive changes (silver linings) in cancer care that emerged during the COVID-19 pandemic and identify challenges that may limit their long-term adoption. METHODS: = 20) were conducted with key opinion leaders from 14 countries. The participants were predominantly members of the International COVID-19 and Cancer Taskforce, who convened in March 2020 to address delivery of cancer care in the context of the pandemic. The Framework Method was employed to analyse the positive changes of the pandemic with corresponding challenges to their maintenance post-pandemic. RESULTS: Ten themes of positive changes were identified which included: value in cancer care, digital communication, convenience, inclusivity and cooperation, decentralisation of cancer care, acceleration of policy change, human interactions, hygiene practices, health awareness and promotion and systems improvement. Impediments to the scale-up of these positive changes included resource disparities and variation in legal frameworks across regions. Barriers were largely attributed to behaviours and attitudes of stakeholders. CONCLUSION: The COVID-19 pandemic has led to important value-based innovations and changes for better cancer care across different health systems. The challenges to maintaining/implementing these changes vary by setting. Efforts are needed to implement improved elements of care that evolved during the pandemic.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".