Where to start: Building a foundation of engagement to support healthcare transformation.
Bibliographic record
Abstract
Introduction: Healthcare transformation is rooted in engagement, engaging people with context and content expertise to shape and co-design programs, services, and systems. The question that is often asked is where do we begin? To offer person centered integrated care, organizations are seeking to understand the needs, experiences, and expertise of those accessing care by building meaningful partnerships with patients, families, and care partners. This workshop will provide attendees with tools, resources, and a roadmap to support building partnerships with people with lived and living experience and engagement models, practices and principles that will support co-designing a healthcare system that is equitable, culturally safe and patient centered. Background: The Greater Hamilton Health Network (GHHN) is one of the first Ontario Health Teams across the province, with a purpose of transforming healthcare in partnership with patients, families, care partners, primary care, local organizations, and the community that is equitable, culturally safe and patient centred. Aims and Objectives:This workshop will provide attendees with an understanding of where to begin when looking to develop an engagement strategy and embed engagement practices and principles into their work at an organizational and system level. With a strong focus on how to develop partnerships with patients, families and carepartners, support patient advisors in their roles, build capacity and integrating lived experience on the team and throughout governance. We will provide examples and outcomes of how engagement has been implemented and successfully used at the GHHN and explore how learnings could be replicated. Emphasis will be placed on how to move from theory into practice. Target audience:This workshop is open to everyone and will work to provide meaningful learning, unlearning, relearning and reflection opportunities for staff, primary care, community organizations, leadership and people with lived and living experience seeking to transform healthcare with engagement as the foundation. Format (timing, speakers, discussion, group work, etc) 5 minutes- Overview of GHHN and introductions 5 minutes- Purpose of Workshop and Objectives 10 minutes-Why Engagement in Healthcare (Benefits, Outcomes) -Patient Advisor example of engagement -Popcorn: common myths of engagement and the real benefits 20 minutes-Building an Engagement strategy and leadership network to support healthcare transformation 10 minutes- Onboarding, integrating lived experience on the team and throughout governance. -Resource package -Activity: Personal Reflection exercise 10 minutes- A culture of engagement that supports providers, patients, families and care partners 20 minutes: Activity: barriers and pathways empathy map exercise (patient, org, staff) 5 minutes- Closing: call to action 5 minutes- Questions Key Learnings/Take away: Patient engagement is a quantifiable measurement of healthcare, offering insights into what is working well and opportunities for growth. This workshop will focus on understanding practical examples of: Models of engagement Engagement strategy Onboarding and capacity building Developing an engagement leadership network Engagement roadmap Meaningful Engagement
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 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 | 0.035 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".