From care to cure: a patient engagement framework for rare disease and orphan drug research
Bibliographic record
Abstract
Background: Rare diseases (RDs) encompass over 6000-8000 conditions, with 94% lacking available therapies. These conditions affect 400 million people globally, including three million Canadians, who face numerous challenges throughout their healthcare journey. Patient engagement (PE) is increasingly recognized as essential for improving outcomes yet remains inadequate in RD and orphan drug research particularly in Canada, where a national strategy for integrating RD patients' perspectives is lacking. To address this gap, this paper presents a Rare Disease Patient Engagement Framework (RDPEF), a structured model designed to support meaningful PE across all levels of healthcare, including research. Objectives: To develop a RDPEF that addresses barriers to engagement, reduces stigma, and incorporates patient experience as a core element in RD and orphan drug research and decision-making. Design: A conceptual framework development study informed by qualitative research and a targeted review of existing PE frameworks. Methods: The RDPEF was developed using a systematic approach that combined a review of existing literature on PE frameworks with new qualitative research on the experiences of RD patients in Canada. Semi-structured interviews examined patients' healthcare journeys, focusing on disease management, access to orphan drugs, and opportunities for engagement. A thematic analysis of the existing literature and interview data identified common challenges, which guided the framework's design. The RDPEF integrates elements from various other PE models, customizes them to the specific needs of RD patients, and emphasizes engagement across the entire orphan drug lifecycle. Results: Thematic findings from qualitative research highlighted limited to no patient involvement beyond clinical trials, significant stigma and discrimination, and the absence of structured engagement in drug review and reimbursement processes. These insights informed the development of the RDPEF, which outlines levels and forms of engagement, guiding principles (including stigma reduction), and mechanisms for integrating patient experience across healthcare, policy, and research domains. Conclusion: The RDPEF is a timely tool for enhancing PE in orphan drug research. By addressing engagement barriers, reducing stigma, and centering patient experience, the framework offers a roadmap for patients, researchers, healthcare providers, and policymakers to create a more inclusive and responsive system for RD patients in Canada.
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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 | 0.112 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.018 | 0.042 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".