Factors that influence community hospital involvement in clinical trials: A qualitative descriptive study
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The successful conduct of randomized clinical trials (RCTs) is often impeded by recruitment difficulties. Community hospitals see large volumes of patients but rarely participate in trials. The objective of this study was to explore how research stakeholders identify and understand the contextual, organizational, research, and individual-level factors that influence the engagement of community hospitals in Ontario to participate in RCTs as partner sites. METHODS: In this descriptive, qualitative study, semi-structured interviews were conducted with a purposeful sample of 18 individuals who are familiar with the processes associated with engaging community hospitals for research or recruiting participants from these sites into trials. Demographic data were summarized using descriptive statistics. The principles of conventional content analysis were used to code, categorize and synthesize the interview data. RESULTS: Informed by participants' descriptions, the results were organized within three unique stages that describe the process of recruitment within community hospitals: (a) community hospital engagement; (b) initiation of the project in the community hospitals; and (c) recruiting patients. The key barriers were the invisibility of the community hospitals to research investigators and the lack of research infrastructure in most of the community hospitals. Increased communication and sharing of resources between academic centers and community hospitals facilitated recruitment across all three stages. CONCLUSION: Our results illustrated a willingness of community hospitals to participate in RCTs, but a lack of capacity for research. Additional efforts by trial coordinating sites are required to recruit community hospitals, but their inclusion improves the generalizability of trial results.
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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 | 0.051 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| 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".