Supporting Discovery and Inquiry: A Canadian Hospital's Approach to Building Research and Innovation Capacity in Point-of-Care Health Professionals
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Building capacity for research and innovation among point-of-care health professionals can translate into positive outcomes from the organization, staff, and patient perspective. However, there is not a widely accepted framework in place across academic hospitals to guide this work and measure impact. This article outlines one Canadian hospital's approach and provides a blueprint with appropriate indicators as a starting point and guide for organizations looking to develop and implement a practice-based research and innovation strategy. METHODS: An adapted framework was utilized to measure and track progress toward achievement of research and innovation strategic goals. The framework outlines key domains for research and capacity development and appropriate metrics. Data are reported from a 4-year period (2014-2018). RESULTS: The evaluation of the practice-based research and innovation portfolio identified several important factors that contribute to the success of embedding this strategy across a large academic teaching institution. These include using a collaborative leadership model, leveraging linkages, partnerships, and collaborations, and recognizing the academic contributions of health professionals engaging in research and innovation. CONCLUSIONS: Engaging those who provide care directly to patients and families in research and innovation is critical to ensuring high-quality health outcomes and patient experience. Creative and innovative funding models, collaborative leadership, and partnerships with key stakeholders to support research and innovation are needed to ensure sustainability.
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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 | 0.105 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.038 | 0.055 |
| Scholarly communication | 0.034 | 0.009 |
| Open science | 0.011 | 0.029 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".