Nimble Approach: fast, adapting, calculating and ethically mindful approach to managing colorectal cancer screening programmes during a pandemic
Bibliographic record
Abstract
OBJECTIVE: To describe a conceptual framework that provides understanding of the challenges encountered and the adaptive approaches taken by organised colorectal cancer (CRC) screening programmes during the initial phase of the COVID-19 pandemic. DESIGN: This was a qualitative case study of international CRC screening programmes. Semi-structured interviews were conducted with programme managers/leaders and programme experts, researchers and clinical leaders of large, population-based screening programmes. Data analysis, using elements of grounded theory, as well as cross-cases analysis was conducted by two experienced qualitative researchers. RESULTS: 19 participants were interviewed from seven programmes in North America, Europe and Australasia. A conceptual framework ('Nimble Approach') was the key outcome of the analysis. Four concepts constitute this approach to managing CRC screening programmes during COVID-19: Fast (meeting the need to make decisions and communicate quickly), Adapting (flexibly and creatively managing testing/colonoscopy capacity, access and backlogs), Calculating (modelling and actively monitoring programmes to inform decision-making and support programme quality) and Ethically Mindful (considering ethical conundrums emerging from programme responses). Highly integrated programmes, those with highly integrated communication networks, and that managed greater portions of the screening process seemed best positioned to respond to the crisis. CONCLUSIONS: The Nimble Approach has potentially broad applications; it can be deployed to effectively respond to programme-specific challenges or manage CRC programmes during future pandemics, other health crises or emergencies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".