Factors influencing community intensive care unit research participation: a qualitative descriptive study
Bibliographic record
Abstract
PURPOSE: Community hospitals account for 90% of hospitals in Canada, but clinical research is mainly conducted in academic hospitals. Increasing community hospital research participation can improve generalizability of study results, while also accelerating study recruitment and increasing staff engagement. We aimed to identify and describe the factors that influence community intensive care unit (ICU) research participation and the development, implementation, and sustainability of a community ICU research program. METHODS: We conducted a qualitative descriptive study using semistructured interviews. Between April 2022 and May 2023, we interviewed a purposeful sample of individuals interested or involved in community hospital research in Canadian community ICUs. We analyzed qualitative data using both conventional content analysis and rapid qualitative analysis. Findings were deductively mapped out using the Ecological Model of Health Behavior. Quantitative survey data were analyzed using descriptive statistics. RESULTS: Participants included 23 health care workers, ten research staff, and five hospital administrators (n = 38) from 20 community hospitals across six provinces in Canada. The main factors associated with community ICU research participation were 1) infrastructure, 2) personnel characteristics, 3) key relationships and connections, and 4) the COVID-19 pandemic. CONCLUSION: In this qualitative descriptive study, participants identified the physical resources, skills, and relationships required to start and sustain a clinical research program in a Canadian community ICU. Our findings suggest that all levels of the Canadian health care system need to invest in strengthening community hospital research capacity to increase research participation.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".