Harnessing Embedded Research as a Strategy for Evidence-Informed Integrated Care Transformation - Lessons Learned from two Embedded Research Training Programs
Bibliographic record
Abstract
Background: Embedded research is emerging as a key strategy to advance learning health systems and evidence-informed integrated care transformation. Embedded research aims to align research with the evidence needs of health system organizations and is intended to help inform organizational decision-making. To build the embedded research workforce and enhance capacity within health system organizations to engage with and draw value from embedded research, embedded research training programs have emerged. This study examines two of the largest embedded research training programs in Canada - the CIHR Health System Impact (HSI) Program and the Ontario Health Team (OHT) Impact Fellows Program - to distill key outcomes, impacts and promising practices for individuals seeking to maximize their value and contribution as an embedded researcher and for organizations seeking to harness embedded research as a strategy for evidence-informed integrated care transformation. Approach: A mixed-methods study design with multiple sources of data and grounded in the Canadian Health Services and Policy Research Alliance Informing Decision-Making Impact Framework was used to assess outcomes and impacts. Data from program documents and website review were used to describe and compare the two programs. Data from program reporting, including fellow and mentor reports and impact narratives, were used to examine outcomes and impacts. The program teams collaborated to review the outcomes and impacts and, from these, co-design a suite of promising practices for individuals and organizations to maximize the value of embedded research towards integrated care transformation. The draft promising practices were shared with integrated care-focused fellows, alum, and mentors in the programs for review and refinement. Their input was incorporated to finalize the promising practices. Results: The CIHR HSI Program and the OHT Impact Fellows Program share similar objectives and core design elements. The programs differ in several key factors, including geographic scope, eligibility criteria for the fellow and the embedding health system organization, duration of the embedded fellowship, prioritized focus areas, and size of cohort. Positive outcomes and impacts are observed in both programs at the level of the fellow (i.e., competency development), the mentor (i.e., expanded academic and system relationships, commitment to research, growth as a mentor), and the embedding health system organization (i.e., increased capacity for embedded research, evidence-informed projects). Several promising practices were identified that focus on developing key core competencies, ensuring a strong start to embedded research relationships, building a supportive culture for embedded research, and co-creating shared vision and goals for success between academic and health system organizations. Implications: The emerging evidence from the CIHR HSI and the OHT Impact Fellows programs suggests that embedded research is a promising tool to help advance evidence-informed integrated care. By co-creating embedded research promising practices with the programs participants, this study generates practical tips and considerations that can help other individuals and organizations optimize their embedded research impacts. This study also contributes to advancing several of the nine pillars of integrated care, including workforce capacity and capability (#5), system leadership (#6), and transparency of progress, results impact (#9).
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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 | 0.279 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.006 | 0.016 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".