Supporting Patient Engagement in Dementia Research via Technology During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The COVID-19 pandemic brings challenges to patient partnerships in research. In-person research meetings with patient partners were prohibited. In this presentation, we outline specific issues we encountered in a patient-led dementia research project, which involved a literature review study and gathering community stakeholders to identify the top 10 local priorities in the development of a dementia-friendly community. We will describe how we found shared solutions to complete the project. In response to COVID, computers and training were provided for patient partners to maintain team connection, plan project activities, conduct team analysis, and host a community workshop in the lockdown time. The drastic shift to virtual research methods created barriers and opportunities for co-research with older people with dementia. Virtual meetings can generate inequities for those who do not have a computer and knowledge in videoconferencing. Practical strategies to overcome barriers to using virtual technologies will be explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.134 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.005 | 0.007 |
| 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".