Creating engagement capable environments for healthcare transformation: A framework for action
Bibliographic record
Abstract
Introduction: Engaging and partnering with patients, caregivers and communities is a key building block for integrated care and health system transformation, yet many organizations lack the skills or capacity to do this work well, and with a focus on creating engagement capable environments. Why Are You Conducting A Workshop? While many health system organizations are committed to engaging and partnering with patients, caregivers and communities, few have carefully considered the competencies, structures and enablers required to support high-quality engagement. In Ontario, the government has undertaken a transformation of the publicly funded health care system through the development of local health care networks called Ontario Health Teams (OHTs). Patient partnership is a core requirement but is being unevenly implemented across OHTs. The need for a supporting framework for these efforts was identified, leading to the development of the Creating Engagement Capable OHTs Framework. The Framework was co-developed by a working group of patient, family and caregiver partners, OHT and government staff and researchers in early 2023. A stepped approach to the development of the framework was taken that allowed for the existing evidence in the field to be considered, along with the needs of different OHT constituencies through consultations, surveys and collaboration. Using a workshop format, we will present the Framework and, using the Ontario context as a case study, we will facilitate discussions with participants about how it might be used in and adapted to other health system contexts to support high-quality engagement and the creation of engagement capable environments. Who Is It For? This workshop will be appropriate for community and patient partners and staff working with health system organizations to support, facilitate or champion patient, caregiver and community engagement. Structure: The structure of the 90-minute workshop will be as follows: 10 mins: Presentation on engagement capable environments and the importance of competencies, structures and enablers for engagement in health system organizations (Presenters) 10 mins: Table group introductions (Full group) 15 mins: Description of the Ontario case study; overview of the framework, including the co-design development process (Presenters) 15 mins: Table group discussions: delegates will review the framework, identify a competency and a support or enabler they feel they are doing well with or struggling with and share their experiences and thoughts with their table allowing for group learning (All) 10 mins: Report back from discussions (All) 15 mins: Table group discussions: delegates will review an assigned competency from the framework and discuss how it could be adapted for their context (All) 10 mins: Report back from discussions (All) 5 mins: Wrap-up (Presenters) How Are You Going To Engage With The Audience? We will engage with the audience through table group discussions, report back and time for questions and answers. How Are You Going To Summarize The Take Home Messages? The take home messages will be summarized at the end of the session and will be posted to our website following the conference.
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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 | 0.075 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.022 | 0.079 |
| Scholarly communication | 0.032 | 0.024 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.018 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".