It’s Time to Increase Community Hospital-Based Health Research
Bibliographic record
Abstract
To the Editor: In 2007, the National Institutes of Health developed a research roadmap,1 which included translational research to bridge the gap between scientific discoveries and clinical practice. On average, only a small proportion (less than 15%) of new scientific discoveries enter routine clinical practice,1 and for those discoveries that do enter routine clinical practice, it takes nearly two decades to reach the general population.2 This is a result of the fact that randomized controlled trials that are generally conducted in academic centers often fail to bridge the gulf from efficacy to effectiveness and do not allow for adaptation of interventions to local circumstances and populations.3 In Ontario, academic research hospitals make up 5% of all hospitals; therefore, most patients receive medical care in nonacademic research hospitals (community hospitals). However, the majority of health research is conducted in academic research hospitals. To bridge the gap between scientific discoveries and clinical practice, we must find ways to increase health research activities in community hospitals. Before we develop strategies to promote community hospital-based health research, we need to understand the barriers that physicians who work in community hospitals face. As the Research Lead team in a community hospital trying to engage physicians in conducting health research, we recognized that community physicians often do not have formal research training or protected research time to conduct research.4 With this in mind, developing a research coaching framework in the community hospital setting may aid in facilitating research through building research skills and confidence amongst staff. Similar to the framework used at Niagara Health in Ontario, where physicians and research teams are coached by the Research Lead office, such a program can effectively run with minimal resources, serving as a platform for physicians and staff with research ideas to form interprofessional teams, thus alleviating time commitments of physicians interested in conducting research. Through regular meetings, teams can be guided through the research process from conducting a literature review through final manuscript preparation. Coaching programs may be an effective stopgap measure for community hospitals with limited funding to build research infrastructure. Jennifer L.Y. Tsang, MD, PhD, FRCPCResearch lead and intensivist, Niagara Health, Niagara, Ontario, Canada, and assistant professor of medicine, McMaster University, Hamilton, Ontario, Canada; [email protected] Katie Ross, MAResearch coordinator, Niagara Health, Niagara, Ontario, Canada.
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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 | 0.025 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.031 | 0.033 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".