Dedicated Research Seed Funding to Support High-Priority Clinician-Led Research: A Survey of Clinician-Researchers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Developing research capacity and supporting research engagement among health professionals are essential parts of bridging the evidence-to-practice gap. The objective of this study was to describe and assess the impact of research seed funding in promoting the engagement of nursing and allied health professionals in leading practice-based research studies within a health care organization. METHODS: An impact assessment survey was administered to health professional grant recipients (n = 30) to collect self-reported observations of changes related to clinical practice, patient care, and organizational priorities as a result of the funded research projects. The electronic survey was developed in collaboration with an interprofessional advisory committee and contained 23 closed- and open-ended questions. Survey data were analyzed descriptively, and responses to open-ended survey questions were used to characterize the funded research activities and the perceived successes and challenges experienced by the research teams. RESULTS: A large proportion of health professional grant recipients reported observing impact from their research studies on clinical practice, organizational priorities, and external networks. The grant funds were used to carry out knowledge dissemination activities such as conference presentations, publications, and sharing findings at professional meetings and networks. The majority of grant expenditures were used for research personnel, and the most common challenge to conducting research was related to the need for protected research time. Most grant recipients were interested in participating in another grant-funded research project in the future. CONCLUSION: Organizational funding and support for research led by health professionals have potential impact on clinical practice, patient care, and organizational priorities. This study provides evidence to support the value of investing in continued and expanded research funding programs for health professionals.
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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 | 0.056 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".