Undertaking Rehabilitation Research During the COVID-19 Pandemic: Emergent Strategies From a Trainee-Faculty Workshop
Bibliographic record
Abstract
Background: The COVID-19 pandemic has disrupted everyday rehabilitation research. Many academic institutions have halted in-person human research including rehabilitation sciences. Researchers are faced with several barriers to continuing their research programs. The purpose of this perspective article is to report the results of an interdisciplinary workshop aimed at understanding the challenges and corresponding strategies for conducting rehabilitation research during the COVID-19 pandemic. Methods: Twenty-five rehabilitation researchers (17 trainees and eight faculty) attended a 2-h facilitated online workshop in to discuss challenges and strategies they had experienced and employed to conduct rehabilitation research during the COVID-19 pandemic. Results: , along with corresponding strategies to these challenges. Researchers experienced disruptions in study outcomes and intervention protocols to adhere to public health guidelines and have suggested implementing novel virtual approaches and study toolkits to facilitate offsite assessment. Participant accessibility could be improved by engaging community stakeholders in protocol revisions to ensure equity, safety, and feasibility. Researchers also experienced barriers to virtual conferences and publication, suggested opportunities for smaller networking events, and revisiting timeframes for knowledge dissemination. Conclusion: This perspective article served as a catalyst for discussion among rehabilitation researchers to identify novel and creative approaches that address the complexities of conducting rehabilitation research during the COVID-19 pandemic 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 | 0.083 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.008 | 0.042 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".