Motivating factors, barriers and facilitators of participation in COVID-19 clinical research: A cross-sectional survey of Canadian community intensive care units
Bibliographic record
Abstract
Only a small proportion of COVID-19 patients in Canada have been recruited into clinical research studies. One reason is that few community intensive care units (ICUs) in Canada participate in research. The objective of this study was to examine the motivating factors, barriers and facilitators to research participation amongst Canadian community ICU stakeholders. A cross-sectional online survey was distributed between May and November 2020. The survey focused on 6 domains: participant demographics, ICU characteristics, ICU research infrastructure, motivating factors, perceived barriers, and perceived facilitators. Responses were received from 73 community ICU stakeholders, representing 18 ICUs. 7/18 ICUs had a clinical research program. Participants rated their interest in pandemic research at a mean of 5.2 (Standard Deviation [SD] = 1.9) on a 7-point Likert scale from 'not interested' to 'very interested'. The strongest motivating factor for research participation was the belief that research improves clinical care and outcomes. The most significant facilitators of research involvement were the availability of an experienced research coordinator and dedicated external funding to cover start-up costs, while the most significant barriers to research involvement were a lack of start-up funding for a research coordinator and a lack of ICU research experience. Canadian Community ICU stakeholders are interested in participating in pandemic research but lack basic infrastructure, research personnel, research experience and start-up funding. Evolution of a research support model at community hospitals, where most patients receive acute care, may increase research participation and improve the generalizability of funded research in Canada.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".