Scholar-in-Residence: An Organizational Capacity-Building Model to Move Evidence to Action
Bibliographic record
Abstract
Quality improvement healthcare leaders recognize that striving for excellence is dependent on a multitude of complex and interactive factors. Translating evidence into clinical practice guidelines, evidence-informed decision-making processes, and policy documents does not, however, guarantee that evidence will reach the point-of-care. This article describes an innovative engagement strategy called the Scholar-in-Residence program. The program represents a model of collaboration between a health region and a university, which is intended to build organizational research capacity while simultaneously facilitating quality in hospital care for seniors. We explain the program and provide implementation details with examples to illustrate how the program builds organizational research capacity at the point-of-care, where healthcare is delivered by professionals, and received by patients admitted to a hospital. By explaining the challenges we encountered, others interested in developing research engagement activities in their health region are assisted and pitfalls are avoided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".