Creating Safe Connections: A Co-Designed E-Learning Module to Advance Equity and Social Accountability in Preventative Primary Care
Bibliographic record
Abstract
Purpose: Lung cancer is the leading cause of cancer-related deaths worldwide and in Canada. Primary care providers (PCPs) play a vital role in incorporating lung cancer prevention and early detection into routine practice. This study outlines the co-design of Creating Safe Connections , an e-learning module developed to build PCPs’ capacity to deliver equity-oriented preventative care. Methods: This manuscript describes the pre-design and co-design phases of the innovation process, guided by the Generative Co-Design Framework for Healthcare Innovation. The pre-design phase established a governance structure comprising patient partners with lived/living experience and interest-holders including PCPs. During the co-design phase, key module priorities and research goals were identified, including barriers to access, stigma and trauma, and operationalizing equity-oriented care. All aspects of the module—its name, logo, content, and knowledge mobilization strategies—were co-developed with the patient partners and health system partners. To inform the e-learning module content, interviews were conducted with community-based PCPs in Ontario, Canada to explore how they apply equity-oriented skills in practice. Interviews were analyzed using deductive content analysis. Results: PCPs’ (five family physicians, two nurse practitioners) interview analysis was informed by the four pillars of Trauma- and Violence-Informed Care: recognizing the impact of trauma and violence; creating emotionally, culturally, and physically safe environments; promoting choice, collaboration, and connection; and adopting a strengths-based, capacity-building approach. These themes shaped the co-design of a Continuing Medical Education-accredited module, which includes video narratives, case studies, a learner’s notebook, and interactive assessments. Conclusion: This work offers a model for the participatory co-design of equity-focused educational interventions that bridge gaps in provider training while aligning with the care needs and priorities identified by structurally underserved populations. The module uses lung cancer screening as a case example to illustrate approaches to addressing inequities in preventative care. Keywords: patient-partnered, accessibility, asynchronous learning, patient-centered care, lung cancer screening, smoking cessation, trauma- and violence-informed care, co-design, lived experience expertise
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".