A Practical Guide to Building, Evaluating and Refining a Multi-Sector Community-Based Integrated Care Model for Seniors
Bibliographic record
Abstract
Background: Integrated care models that bring together health and social care services in the communities where people live are necessary to meet people’s needs in an effective, person-centred, and sustainable manner. However, designing, implementing, and evaluating these models remain challenging particularly given the complexity of need, services, and partnerships. Objectives: Over the last three years, we utilized a learning health system approach to design, implement, evaluate, and revisit our integrated care model that aims to integrate health and social care for seniors, with a focus on those facing socioeconomic or other challenges. The Community Wellness Hub is an alliance of health and social service providers that coordinate and deliver services to seniors. The Hub is located in affordable housing buildings and provides services to individuals who reside in the building and surrounding area. The aim is to enable members to lead healthy and fulfilling lives in the community by proactively addressing health and wellness needs and reducing health crises requiring acute care. Services provided are an intersection of three systems: health care, housing, and social care, spanning 15 organizations. In this workshop, we share the journey of the Community Wellness Hub from inception to date. We will reflect on facilitators, challenges, and learnings in three main areas: building and implementing the hub, evaluating the implementation, and enacting the results of the evaluation. Proposed Audience: Proposed participants include policy makers, program designers, evaluators, quality improvement specialists, patients, and caregivers as well as researchers interested in designing and evaluating complex integrated care initiatives. Structure: •The first 5-10 minutes of the workshop will be a round table introduction •Then 5 minutes for introducing the Community Wellness Hub and the agenda of the workshop to the participants •The last 10 minutes will be for summarizing the lessons learned and take-home messages. Similarities and variations amongst jurisdictions will be reflected on based on the participants •Then the rest of the time will be divided into three equal parts. The first will cover the creation of the hub, then the evaluation and finally enacting of the evaluation results into actionable steps. Each of the three sections will start by an open question inviting the audience to work in small groups to answer this question. These questions are: what are the key elements when creating a hub model via a partnership that spans health, social and community care? How to evaluate the implementation of an integrated hub model? How to enact the evaluation results? •After each group discussion, we will connect to reflect on the various approaches. Following that the presenters will share the approach they used within the hub highlighting resources, methods, tools, and practical tips. Outcome: By the end of the workshop, participants are expected to have learned some practical tips around designing, implementing, evaluating and utilizing the evaluation results in the context of integrating health and social care that may be applied to their local programs or initiatives.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.024 |
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".