Action at a Distance: Geriatric Research during a Pandemic
Bibliographic record
Abstract
BACKGROUND: "Action at a distance" may be the new norm for clinical researchers in the context of the COVID-19 pandemic that may require social distancing for the next 18 months. We must minimize face-to-face contact with vulnerable populations. But we must also persist, adapt, and help our older patients and study participants during the pandemic. METHODS: Clinical researchers have an obligation to help, and we can. Recommendations for clinical researchers working with older adults during the COVID-19 pandemic are discussed. RESULTS: Implement technology now: Minimize face-to-face contact with participants by utilizing digital tools, such as shifting to electronic informed consent and digital HIPAA-compliant tools such as e-mailed surveys or telehealth assessments. Assess the psychological and social impact of COVID-19: How are participants coping? What health or social behaviors have changed? How are they keeping up with current events? What are they doing to stay connected to their families, friends, and communities? Are their healthcare needs being met? Current studies should be adapted immediately to these ends. Mobilize research platforms for patient needs: Leverage our relationships with participants and rapidly deploy novel clinical engagement techniques such as digital tools to intervene remotely and reduce the negative effects of social isolation on our participants. Equip research staff with tangible resources, and provide timely population-specific health information to support patients and healthcare providers. CONCLUSIONS: We have an opportunity to make an impact on our older adult patients now as this pandemic continues to unfold. Above all, clinical researchers need to continue working, to help as many people as possible through the crisis. J Am Geriatr Soc 68:922-925, 2020.
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 | 0.084 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".