Developing a toolkit for building a community hospital clinical research program
Bibliographic record
Abstract
PURPOSE: Although health research in Canada is primarily conducted in academic hospitals, most patients receive their care in community hospitals. The benefits of increasing research capacity in community hospitals include improved study recruitment, increased generalizability of results, broader patient access to novel therapies, better patient outcomes, enhanced staff satisfaction, and improved organizational efficiency. Nevertheless, building research programs in community hospitals remains challenging because of a lack of support and expertise. To address this gap, we developed a toolkit to help community hospital professionals build and sustain their community hospital research programs. SOURCE: The toolkit was developed by the Canadian Community Intensive Care Unit Research Network (CCIRNet), a group of clinician-researchers and research staff from community hospitals across Canada who have experience building community hospital research programs. Feedback from a concurrent qualitative study of Canadian community critical care professionals informed the toolkit's design. PRINCIPAL FINDINGS: The CCIRNet toolkit outlines five stages of community hospital clinical research program development: 1) building a research team and gaining support, 2) developing a new research program, 3) choosing a first research study, 4) getting the study up and running, and 5) sustaining a research program. Feedback from qualitative interviews emphasized the need for a step-by-step approach, frequently asked questions, and essential resources. Accordingly, each stage is structured in a question-and-answer format and includes relevant resources for each section. CONCLUSION: The CCIRNet toolkit is a practical resource for establishing research programs in community hospitals. The toolkit may increase research participation and support clinical research capacity building in community hospitals.
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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 | 0.117 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".