Barriers to multisite research in Canada: Experiences from a minimal risk COVID-19 study
Bibliographic record
Abstract
The ability to provide timely evidence-informed health care depends on high quality clinical research that responds to current needs and health crises. Canadian researchers doing many types of research have faced significant challenges obtaining timely research ethics board and institutional approvals for research causing premature termination of studies, study delays, wasted resources and, crucially, missed opportunities to improve clinical care and outcomes. To illustrate such challenges, we refer to the minimal risk, multisite observational study we are currently conducting, examining the long-term respiratory health effects of COVID-19. As a COVID-19 research study, it was purportedly prioritized for review; however we experienced long delays in study approval. Three main factors contributed: lengthy and repetitive REB review processes, discrepancies in REB and institutional requirements, and multidepartment approval requirements. Delays in research study approval impede new knowledge and, ultimately, improvements in patient care and health. This in itself, represents an ethical dilemma that we can no longer ignore.
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 | 0.113 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.039 | 0.014 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".