Toward equity-oriented cancer care: a Strategy for Patient-Oriented Research (SPOR) protocol to promote equitable access to lung cancer screening
Bibliographic record
Abstract
BACKGROUND: Screening for lung cancer with low dose CT can facilitate the detection of early-stage lung cancers that are amenable to treatment, reducing mortality related to lung cancer. Individuals are considered eligible for lung cancer screening if they meet specific high-risk criteria, such as age and smoking history. Population groups that are at highest risk of lung cancer, and therefore, the target of lung cancer screening interventions, are also the least likely to participate in lung cancer screening. This can lead to a widening of health inequities. Deliberate effort is needed to both reduce lung cancer risk (through upstream interventions that promote smoking cessation) as well as midstream interventions that promote equitable access to lung cancer screening. METHODS: This protocol paper describes an equity-informed patient-oriented research study. Our study aims to promote equitable access to lung cancer screening by partnering with patients to co-design an e-learning module for healthcare providers. The learning module will describe the social context of lung cancer risk and promote access to lung cancer screening by increasing equity at the point of care. We have applied the Generative Co-Design Framework for Healthcare Innovation and detail our study processes in three phases and six steps: Pre-design (establishing a study governance structure); Co-design (identifying research priorities, gathering and interpreting data, co-developing module content); and Post-design (pilot testing the module and developing an implementation plan). DISCUSSION: Patient engagement in research can promote the design and delivery of healthcare services that are accessible and acceptable to patients. This is particularly important for lung cancer screening as those at highest risk of developing lung cancer are also those who are least likely to participate in lung cancer screening. By detailing the steps of our participatory co-design journey, we are making visible the processes of our work so that they can be linked to future outcomes and related impact, and inform a wide range of patient co-led processes.
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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 | 0.020 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".