Re-engaging in Aging and Mobility Research in the COVID-19 Era: Early Lessons from Pivoting a Large-Scale, Interdisciplinary Study amidst a Pandemic
Bibliographic record
Abstract
Abstract In the wake of the COVID-19 pandemic, those planning and conducting research involving older adults have faced many challenges, in part because of the public health measures in place. This article details the early steps and corresponding strategies implemented by our multidisciplinary team to pivot our large-scale aging and mobility study. Based on the premise that all current and emerging research in aging has been impacted by the pandemic, we propose a continuum approach whereby the research question, analysis, and interpretation are situated in accordance with the stage of the pandemic. Using examples from our own study, we outline potential ways to partner with older adults and other stakeholders as well as to encourage collaboration beyond disciplinary silos even under the current circumstances. Finally, we suggest the formation of a Canadian-led consortium that leverages cross-disciplinary expertise to address the complexities of our aging population in the COVID-19 era and beyond.
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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 | 0.276 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.042 | 0.035 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.008 | 0.049 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".