Increasing research capacity in Canadian community hospitals: an intrinsic descriptive case study
Bibliographic record
Abstract
BACKGROUND: Canada's clinical research landscape is limited by minimal community hospital engagement. However, research participation in community hospitals may increase the speed of trial enrolment, enhance the generalizability of results and accelerate knowledge translation to community hospitals, where most Canadians receive care. Two identified barriers to community hospital participation are limited financial support and a lack of research mentorship. METHODS: This study is an intrinsic descriptive case study describing the impact of 1 year of research funding from the Canadian Critical Care Trials Group (CCCTG) and creation of a community of practice on research participation in 19 community hospitals. Thematic analysis was used to systematically identify themes from semistructured interviews and documents. RESULTS: A total of nine individuals (physician research lead, n = 7; research staff, n = 2) participated in semistructured interviews between April and September 2023. Community of practice meeting minutes (n = 7), emails (n = 22), status reports (n = 21) and field notes (n = 7) were analysed alongside interview transcripts. Funding enabled community hospitals to hire research staff, sustain research programs, increase the number of clinical trials they were running and develop research policies. The community of practice facilitated reciprocal learning and networking that positively impacted research programs and produced a tangible output: a toolkit to help community hospitals build clinical research programs. Contextual influences on community hospital research activities were identified as: (1) system characteristics, (2) clinical trial design, (3) local context and (4) individual characteristics. CONCLUSIONS: The perception of participants was that the CCCTG funding and community of practice positively influenced research activities in community hospitals. Lessons learned include the need to: (1) leverage the power of connections among community hospitals to expand linkages, enabling further knowledge transfer, (2) work with trialists on clinical trial design to facilitate implementation and (3) create resources to support community hospitals with building and sustaining research programs, including resources to foster engagement in hospitals without historic research participation. Our findings highlight the importance of context, including local populations, organizational research culture, provincial health systems and research funding structures, which need to be considered during research program implementation.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.038 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".