Systems mapping: a novel approach to national lung cancer screening implementation in Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Lung cancer screening with low-dose computed tomography has been started in some high-income countries and is being considered in others. In many settings uptake remains low. Optimal strategies to increase uptake, including for high-risk subgroups, have not been elucidated. This study used a system dynamics approach based on expert consensus to identify (I) the likely determinants of screening uptake and (II) interactions between these determinants that may affect screening uptake. Methods: Consensus data on key factors influencing screening uptake were developed from existing literature and through two stakeholder workshops involving clinical and consumer experts. These factors were used to develop a causal loop diagram (CLD) of lung cancer screening uptake. Results: The CLD comprised three main perspectives of importance for a lung cancer screening program: participant, primary care, and health system. Eight key drivers in the system were identified within these perspectives that will likely influence screening uptake: (I) patient stigma; (II) patient fear of having lung cancer; (III) patient health literacy; (IV) patient waiting time for a scan appointment; (V) general practitioner (GP) capacity; (VI) GP clarity on next steps after an abnormal computed tomography (CT); (VII) specialist capacity to accept referrals and undertake evaluation; and (VIII) healthcare capacity for scanning and reporting. Five key system leverage points to optimise screening uptake were also identified: (I) patient stigma influencing willingness to receive a scan; (II) GP capacity for referral to scans; (III) GP capacity to increase patients' health literacy; (IV) specialist capacity to connect patients with timely treatment; and (V) healthcare capacity to reduce scanning waiting times. Conclusions: This novel approach to investigation of lung cancer screening implementation, based on Australian expert stakeholder consensus, provides a system-wide view of critical factors that may either limit or promote screening uptake.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it