Researcher and patient experiences of co-presenting research to people living with systemic sclerosis at a patient conference: content analysis of interviews
Bibliographic record
Abstract
Abstract Background Patient engagement in research is important to ensure research questions address problems important to patients, that research is designed in a way that can effectively answer those questions, and that findings are applicable, relevant, and credible. Yet, patients are rarely involved in the dissemination stage of research. This study explored one way to engage patients in dissemination, through co-presenting research. Methods Semi-structured, one-on-one, audio-recorded interviews were conducted with researchers and patients who co-presented research at one patient conference (the 2022 Canadian National Scleroderma Conference) in Canada. A pragmatic orientation was adopted, and following verbatim transcription, data were analyzed using conventional content analysis. Results Of 8 researchers who were paired with 7 patients, 5 researchers (mean age = 28 years, SD = 3.6 years) and 5 patients (mean age = 45 years, SD = 14.2 years) participated. Researcher and patient perspectives about their experiences co-presenting and how to improve the experience were captured across 4 main categories: (1) Reasons for accepting the invitation to co-present; (2) Degree that co-presenting expectations were met; (3) The process of co-presenting; and (4) Lessons learned: recommendations for co-presenting. Conclusions Findings from this study suggest that the co-presenting experience was a rewarding and enjoyable way to tailor research dissemination to patients. We identified a patient-centred approach and meaningful and prolonged patient engagement as essential elements underlying co-presenting success.
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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 | 0.045 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".