Using Social Media and Web-Based Networking in Collaborative Research: Protocol for the Geriatric Medicine Research Collaborative
Bibliographic record
Abstract
BACKGROUND: Traditional pathways to promote research collaboration typically take years to expand beyond individual institutions. Social media and online networking provide an innovative approach to promote research collaboration. OBJECTIVE: The objective of this paper is to present the formation of the Geriatric Medicine Research Collaborative, United Kingdom - a national trainee-led research collaborative. This collaborative aims to facilitate research projects that will directly benefit older patients, improve research skills of geriatric medicine trainees, and facilitate recommendations for health care policy for older adults. METHODS: Our methods of collaboration comprised trainee-led meetings regionally and at national conferences, email communication, direct uploading of project material to our website, social media, and virtual meetings. Structured use of local, regional, and network leads has facilitated this collaboration. Having a clear virtual presence has been the key to the rapid development of the network. RESULTS: The use of social media and online networking encouraged the involvement of multiple regions early in the development of the collaborative and allowed rapid dissemination of project ideas. This facilitated the collection of large datasets and enhanced scientific validity of project outcomes. Furthermore, this has the potential to transform geriatric medicine research, as older patients have been historically excluded from large commercial trials due to multimorbidity, frailty, and cognitive impairment. CONCLUSIONS: Perceived limitations to predominantly online or virtual collaboratives, including reduced accountability, and loss of interpersonal relationships are balanced by increased trainee engagement, high frequency of communication, and rapid access to a breadth of expertise. Utilization of virtual communication has the potential to lead to future interspecialty, interprofessional, and international collaboration, and to accelerate research that improves outcomes for older adults.
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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 | 0.081 | 0.088 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.068 | 0.023 |
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".