Barriers to participation in biosampling-based translational research: A cross-sectional survey of Canadian critical care researchers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Collection of biosamples for translational research studies is vital for understanding biological pathways, discovering disease-related biomarkers, and identifying novel therapeutic targets. However, a lack of infrastructure for sample procurement, processing, storage, and shipping may hinder the ability of clinical research units to effectively engage in translational research. The purpose of this study was to identify the barriers to biosampling-based translational research in the critical care setting in Canada. METHODS: We administered an online survey to members of the Canadian Critical Care Trials Group (CCCTG), the Canadian Critical Care Translational Biology Group (CCCTBG), and the Canadian Critical Care Research Coordinators Group (CCCRCG). The survey focused on participants' personal experience of biosampling research, research infrastructure, motivating factors, and perceived barriers. RESULTS: We received 59 responses from 31 sites, including 6 community intensive care unit (ICU) sites. The overall response rate was 11.3%. The majority of respondents were research coordinators (44%), followed by clinician-investigators (33.8%), graduate students (10.2%), and PhD-investigators (8.5%). Although most (63.8%) respondents reported an interest in participating in translational research, they also reported that their ICUs were currently contributing to a third of the number of translational studies compared to clinical studies. For respondents with experience in participating in translational research studies, the most common barriers were lack of funding, lack of time, and insufficient research staff. For respondents without previous experience, the perceived facilitators were more interest from their research group, improved training/mentorship, increased funding, and better access to laboratory equipment. CONCLUSIONS: Our survey found that the majority of participants were interested in and recognize the value of participating in biosampling-based translational research but lacked funding, time, and research personnel trained in biosampling protocols. Our survey also identified factors that might encourage participation at new sites. Addressing these barriers will be a key step towards increasing translational research capacity across 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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".