Building Evaluation Capacity for Integrated Care: Lessons from an Embedded Researcher Program
Bibliographic record
Abstract
Introduction: Evaluation has an important role to play in supporting integrated care. By collecting data early and often, care teams can better learn and adapt as new models of care are implemented. While the benefits of evaluation are well known, organizations may lack the capacity to conduct these well. This is particularly true of smaller organizations, where research is often seen as a luxury rather than a ‘must have.’ This paper presents lessons from the OHT Impact Fellows, a training program that places postdoctoral fellows in healthcare organizations where they support implementation and evaluation of integrated care projects. Since its launch in 2021, the program has placed 22 fellows in health teams across Ontario, Canada. Drawing on the collective experience of these fellows, we offer suggestions on how to build evaluation capacity for integrated care. Background: Introduced by the provincial government in 2019, Ontario Health Teams (OHTs) are a new way of organizing care in Ontario. The primary goal of OHTs is to deliver care in a more integrated way, with care providers in different sectors (e.g., hospitals, primary care, home and community care) working as one coordinated team. To support their development, the government has funded several support programs, including the OHT Impact Fellows. Designed with input from researchers, funders (government), and knowledge users (clinicians, health leaders, and patients), this program provides on-the-ground support tailored to the needs of host OHTs. The Program: Each year, OHTs are invited to submit expressions of interest to host a research fellow. Soon afterwards, a call is issued for fellowship applicants. After a rigorous selection process, fellows are matched with a host OHT based on their mutual fit. This process ensures that the skills and interests of fellows match the needs of their host organizations. Fellows then spend one year embedded in an OHT, supporting evaluation within and learning across OHTs. Fellows are matched with a host and academic mentor and supported with ongoing training and professional development opportunities. At the end of their fellowship, participants provide detailed feedback on the program through a survey. Results: Based on this survey data, here are some learnings so far: 1. Fellows were seen as highly impactful in supporting local projects and building OHT capacity. They were seen as moderately impactful in supporting learning across OHTs. 2. Fellows were extremely productive, sharing knowledge through conference presentations (>30), technical reports (>20), and internal briefings (>90) in one year. 3. Many fellows worked on projects that engaged patients and caregivers, often using a co-design approach. Patient engagement and co-design are among the top topics for which fellows have requested additional training. 4. Building evaluation capacity is as much about culture as analytical ability. For some organizations, ‘evaluation’ is a scary word that needs to be demystified before it can be embraced. Next Steps: We hope to offer a third round of fellowships in 2023. Audience: This presentation will be of interest to clinician-scientists, health leaders, researchers, and others interested in evaluation and its use in integrated care settings.
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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 | 0.195 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.008 | 0.041 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".