Relational Approaches in Patient-Oriented Research During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has exposed the fragile state of patient involvement in research. The involvement of the most vulnerable population (older people with dementia) in research was even more challenging. This presentation outlines challenges my research team encountered in patient-led projects (older people with dementia) and describes how we found creative strategies to set up and complete research during the time of pandemic. I will describe how the team applied collaborative participatory principles to engage a team with diverse backgrounds in the lockdown time to maintain research progress. Patient partners in my research team actively led recruitment, research planning and decision-making, team analysis and knowledge exchange. University students in our research team helped to make technology easy to use for our patient partners. The friendly, flexible and accessible exchange between students and patient partners reinforced the importance of a respectful relational approach in patient-oriented research.
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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 | 0.252 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.057 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".