Going From Zero to 100 in Remote Dementia Research: A Practical Guide (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Remote approaches for dementia research are required in the era of COVID-19, but moving a research program from in person to remote involves additional considerations. We recommend using outcome measures that have psychometric properties for remote delivery, and we recommend against adapting in-person scales for remote delivery without evidence for psychometric equivalency. We suggest remote research designs that maximize benefit for participants, which could have implications for control groups. Researchers should plan for flexibility in their methods for remote research and must not assume all participants will be able to videoconference; telephone-only research is possible. We recommend performing an assessment of information communication technology infrastructure and prior exposure to this technology with each participant before making a final choice on remote methods for research. In general, researchers should adapt their methods for remote research to each participant rather than requesting participants to adapt to the researchers. Screening for sensory loss should be conducted, and the impact of this on the use of technology for remote research should be considered. In this viewpoint, we detail how individualized training is required prior to engaging in remote research, how training plans interact with cognitive impairments and, finally, the steps involved in facilitating technology-based remote data collection.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.030 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.158 | 0.196 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".