Patient partner engagement in dementia research during the COVID-19 pandemic and beyond
Bibliographic record
Abstract
While older people living with dementia have been disproportionally affected by COVID-19, the devastating effect of the COVID-19 pandemic has also significantly impacted patient partner involvement in research. Engaging people with dementia in research (from research planning, data collection, team analysis, and dissemination) helps produce relevant, meaningful outcomes and reduce disparities. The COVID-19 pandemic has exposed the fragile state of patient involvement in research and forced researchers to develop innovative ideas to engage and partner with patients. During outbreaks, patient partners (people living with dementia) could not enter the clinical sites for in-person research meetings. The drastic shift to online platforms in research posed difficulties for research teamwork. One of the strategies used by our research team to support patient partner involvement in research activities during the pandemic was a telepresence robot which was useful for conducting remote interviews. However, despite the rapid development of technological aid to facilitate research during the pandemic, remote research work gave rise to a unique set of challenges. This chapter depicts specific issues our research team encountered while conducting research in Canadian Long-Term Care (LTC) homes during the pandemic. Coping strategies are described, such as sending patient partners new computers and headphones, providing virtual technology training, finding new ways to maintain team connections, and supporting patient partners during lockdowns. Finally, reflections, lessons learned, and practical tips about applying Collaborative Action Research (CAR) principles to continue supporting patient partners as active research contributors are discussed.
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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 | 0.052 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".