Returning Research Results to Patients with Rheumatoid Arthritis: A Patient-Driven Knowledge Translation Strategy from the Canadian Early Arthritis Cohort (CATCH)
Bibliographic record
Abstract
Objectives Sharing research findings with patients is crucial to enhance transparency, foster trust, and engage research participants and to address the evidence-to-practice gap.[1] However, the method and timing of knowledge translation (KT) are essential to communicating research results[2] to patients from different linguistic and cultural backgrounds. Our goal was to create a tailored KT strategy to communicate key findings from the Canadian Early Arthritis Cohort (CATCH) to participants and the wider rheumatoid arthritis (RA) community. Methods An initial list of topics relevant to RA patients was generated based on the lived experiences of the KT specialists with RA and further refined based on various input (polling, discussions with RA patients and Scientific Advisory Committee (SAC) members). These topics were mapped to available CATCH research projects and used to develop videos, plain language research summaries (PLRS), and social media messaging. KT specialists oversaw video production involving SAC members and RA patients, adapting interview questions to ensure relevance for the patient audience. PLRS were written using the guidance developed by Clinical Trials Ontario.[3] Topics were used to compile evidence-based resources from credible arthritis organizations. The KT products were disseminated on various social media platforms, like X (earlyarthritis), YouTube (@canadianearlyarthritiscoho928), Instagram (@earlyarthritis), and CATCH website ( www.earlyarthritis.ca ). Results We have developed 100 English and French language videos covering key topics such as disease/symptom management and preventive health (infections/vaccines). We produced 15 PLRS based on CATCH research and compiled patient resources across 8 themes. All website content and some social media and video content (available at https://bit.ly/3stynpR ) were made available in French. To share or return research results with CATCH participants, an appointment card which included information about KT strategies was developed and shared with over 3000 participants. To measure the uptake of KT efforts, we monitored social media and website metrics: the YouTube channel has accumulated over 115,000 views and 614 subscribers while X has 1100 followers and Instagram has 225 followers. Since February 2024, the CATCH website has received 3000 unique visitors and close to 15,000 page views. Conclusion Our KT approach addresses an important gap by sharing research knowledge with patients and the public through a strategy that incorporates plain language and multimedia content. Future efforts will focus on evaluating the effectiveness of this KT strategy through patient feedback, engagement metrics, and assessing impact on patient knowledge and behavior. [1.] Straus SE. CMAJ 2009;181(3-4):165-8. [2.] Angrist M. Per Med 2011;8(6):651-657. [3.] Clinical Trials Ontario. https://ctontario.ca/resources/participant-experience-toolkit/plain-language-result-summaries/ Toronto: Clinical Trials Ontario; [cited 2024 Sep 23].
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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 | 0.042 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".