Building capacity for patient-engagement in neuromuscular disease research: A network project
Bibliographic record
Abstract
Patient-oriented research is increasingly recognized as an important methodology in health sciences. Benefits of patient engagement include aligning research priorities to those living with health conditions, developing better recruitment strategies and protocols, and integrating findings more meaningfully. However, for research teams to work well with patient-partners, training for all stakeholders is needed. While training exists, none consider the uniqueness of the neuromuscular disease experience. To address this gap, our team of researchers, clinicians, and patient-partners collaborated to increase the capacity for patient-engagement in neuromuscular disease research. Our methods included: 1) conducting a landscape of available resources, and 2) using adult education principles and a backwards design process to develop unique training modules. The result is an online platform with three modules focusing on the neuromuscular disease context, addressing the inclusion, diversity, equity, and accessibility needs of those with neuromuscular diseases, and building teamwork skills. Early evaluation of the first two modules indicates high satisfaction and knowledge gain. Through this process, we have learned about the barriers of patient-oriented research, how to support the required system culture shift, and how to plan for long-term sustainability.
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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 | 0.095 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".