MétaCan
Menu
← Back to cohort
Record W4390939783 · doi:10.5334/ijic.icic23305

Building Evaluation Capacity for Integrated Care: Lessons from an Embedded Researcher Program

2023· article· en· W4390939783 on OpenAlexaffabout
Patrick Feng, Meghan McMahon, Angela Del Monte, Ross Baker

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsCanadian Institutes of Health ResearchOntario College of Art and DesignUniversity of Toronto
Fundersnot available
KeywordsGovernment (linguistics)Health careIntegrated careBusinessPublic relationsNursingMedical educationKnowledge managementMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.195
metaresearch head score (Gemma)0.185
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.195
Threshold uncertainty score0.993

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1950.185
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0170.025
Scholarly communication0.0180.024
Open science0.0080.041
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0110.002

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.

Opus teacher head0.256
GPT teacher head0.562
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Integrated Care→Same topicPrimary Care and Health Outcomes→French-language works237,207→