Lessons Learned from ICU Research During a Pandemic: A Multisite Qualitative Study to Inform Research Innovation
Bibliographic record
Abstract
Abstract Purpose Emergency conditions such as the COVID-19 pandemic pose complex scientific and ethical challenges for researchers, which must be addressed to optimise efficiencies in trial conduct. Our purpose was to examine key factors essential to creating an agile system responsive to the rapidly changing research and clinical environment and to understand how we might learn from this unique experience to bolster research capacity in future pandemics.Methods Our evaluation employed robust qualitative descriptive methodology which comprises an approach for gathering information directly from those experiencing an event or process and flexible application of theoretical frameworks to assist in the analysis. Data was collected through individual interviews of key research stakeholders and our thematic analysis was informed by the Consolidated Framework for Implementation Research (CFIR).Results Over 17 months, we interviewed 64 participants across four research sites. Our findings uncovered key challenges in each of the 5 constructs of the CFIR: the outer setting, the inner setting, intervention characteristics, individual characteristics and rapid implantation processes which were put in place to allow crucial research to happen under extenuating circumstances.Conclusion Our data demonstrate the pandemic-magnified shortcomings of a precarious research infrastructure both in local ICUs and at the national level. Focusing investment on more efficient research platforms and administration, considering research and data sharing capacity and patient and family experience in protocol development, building a robust research workforce, and revamping the funding architecture at all levels are important lessons to promote seamless delivery of critical care research in pandemic conditions.
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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 | 0.127 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.029 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".