COVID-19 and interdisciplinary research: What are the needs of researchers on aging?
Bibliographic record
Abstract
COVID-19 has had an extreme effect on older people. Now more than ever we need collaborative approaches to address complex issues within research on aging. However, the pandemic has dramatically changed the way we conduct, interact, and organize research within interdisciplinary groups. This paper describes a case study of how an interdisciplinary institute for research on aging has managed the process of change during COVID-19 restrictions. A design lead, researcher centered approach was used to understand the needs of researchers as they adapted across 6 months. Firstly, an online survey (n=51) was conducted to understand the scope of change and needs. The survey found broad themes ranging from assistance with finding additional funding to adjusting current research proposals. Following the survey, two Co-Design Sessions were conducted. The first session (n=53) diverged thinking to scope ideas from the survey and actionable themes were created. The second session (n=36) was conducted to converge thinking and focus on solutions based on one of these themes. The results revealed a diversity of ideas addressing the needs of interdisciplinary researchers in aging. These ideas spanned from exploring the capacity to do research remotely and creating virtual collaboration spaces to rethinking stakeholder engagement. Received: 1 July 2021Accepted: 12 October 2021
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 | 0.258 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.039 | 0.031 |
| Scholarly communication | 0.038 | 0.030 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.017 | 0.018 |
| 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".