Co designing an e-learning module for primary care providers to improve equity in access to lung cancer screening
Bibliographic record
Abstract
Context: To improve equity in lung cancer care, primary care providers need to provide safe and timely referrals to lung cancer screening for people experiencing stigma and discrimination. Objective: The purpose of our study was to promote equitable access to lung cancer screening by co-designing an educational intervention (an e-learning module) that will build knowledge and skills in primary care providers to deliver equity-oriented lung cancer screening. Study Design and Analysis: We have applied the Generative Co-Design Framework for Healthcare Innovation and conducted our study processes. Setting or Dataset: Our dataset is in three phases: Pre-design; Co-design; and Post-design Population Studied: Primary care providers in Ontario. Intervention/Instrument: An e-module, named “Creating Safe Connections: Practical Strategies to Support Lung Cancer Screening.” Outcome Measures and Results: Pre-design: This equity-informed patient-centered research was led by an interdisciplinary team of provincial and national health system stakeholders, healthcare providers, and a patient advisory council representing diverse lived experiences. All team members played an equal role in project design, conduct, analysis, and dissemination. Co-design: We identified research priorities and conducted qualitative interviews with primary care providers in Ontario to understand learning gaps and needs. Using the Trauma- and violence-informed (TVIC) care core competency framework, we mapped learning needs onto the following areas: 1) being trauma and violence aware in lung cancer screening discussions (TVIC Principle 1); creating safe spaces and interactions (TVIC Principle 2); and building on a person’s strengths and collaborating to make realistic choices (TVIC Principles 3 & 4). We co-developed learning module content consisting of case-studies, patient videos and knowledge checks and packaged the material into an e-module format, titled: Creating Safe Connections: Practical Strategies to Support Lung Cancer Screening. Post-design: The e-module, currently in pilot testing, is hosted on the EQUIP Healthcare website and is accessible to all Canadian users through the University of British Columbia learning platform. Conclusion: We demonstrate how interdisciplinary partnerships that are inclusive of underserved patient communities can lead to the co-design of patient-centered cancer education to improve lung cancer-related health outcomes.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".