Systems mapping: a novel approach to national lung cancer screening implementation in Australia
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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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".