The legacy of the COVID-19 pandemic on critical care research: A descriptive interview study
Bibliographic record
Abstract
Background: The COVID-19 pandemic challenged both research and clinical teams in critical care to collaborate on research solutions to new clinical problems. Although an effective, nationally coordinated response helped facilitate critical care research, reprioritisation of research efforts towards COVID-19 studies had significant consequences for existing and planned research activity in critical care. Aims: Our aim was to explore the impact of the COVID-19 pandemic research prioritisation policies and practices on critical care research funded prior to the pandemic, the conduct of pandemic research, and implications for ongoing and future critical care research. Methods: We undertook a descriptive qualitative study recruiting research-active clinician researchers and research delivery team members working in critical care. We conducted digitally recorded, semi-structured interviews in 2021–2022. Framework Analysis was used to analyse the data. Results: We interviewed 22 participants comprising principal investigators, senior trial coordinators and research delivery nurses from across the UK. Six themes were identified: Unit, organisational and national factors; Study specific factors; Resources; Individual/clinician factors; Family/patient factors; Contextual factors. These themes explained how a nationally coordinated response during the pandemic affected individuals, studies and wider organisations in managing the research response in critical care, highlighting future implications for critical care research. Conclusion: Harnessing the collective response seen in the COVID-19 pandemic in critical care could better support integration of research activity into routine critical care activities. Future endeavours should focus on workforce preparations, contingency planning, strategies for study prioritisation and integration of research as part of the continuum of clinical care.
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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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".