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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".