Strategies for the implementation of Goal Oriented Care through the lens of Valentijn’s Rainbowmodel for integrated care.
Bibliographic record
Abstract
Short: With the use of the Rainbowmodel of Valentijn as a guide you will discover how to develop an integrated implementation strategy for goal oriented care across the system as a catalyst for person centered system integration. Why: Health systems are in constant transformation to optimize integrated care delivery for patients with complex needs. One of the suggested strategies for person centered integrated care delivery (PC-IC) is an explicit focus on patients’ personal goals throughout care delivery. This should reorient the focus on disease oriented quality indicators to outcomes that matter to people. To get this focus on patients goals implemented throughout the system, actions in practice, research, and policy are required. An approach that highlights the importance of explicitly starting from the patients' personal goals is goal oriented care (GOC). When we look at the implementation of GOC through the lens of Valentijn's rainbow model we can identify different strategies for sustainable and integrated implementation of this person centered approach. Audience: Everyone with interest in person-centered integrated care and/or goal-oriented care and anyone on how this approach can be supported and adopted throughout the system. Structure Introduction 20’ Group work 35’ Plenary discussion 25’ Wrap up and key messages 15’ How to engage The workshop will start with a short introduction on why GOC could serve as a catalyst for person-centered system integration. Consequently the speakers will walk the group through the different levels (arcs) of Valentijn's model for integrated care showing examples of how different GOC projects in research, practice, teaching and policy are conducted at the different levels and how they focus on functional and/or normative integration. Next, the group will be divided in small groups who each will focus on a separate arc from the rainbow model (the citizen/patient/informal caregivers level, the providers/clinical level, the interprofessional team level, the organizational level and the policy level) A moderator will facilitate a group discussion about how GOC could be supported at that specific level and how. The rainbow model will act as a visual aid and will be used as a surface for post-it notes, to further facilitate interaction. After the brainstorm in small groups, the ideas from the different arcs will be reported back in a plenary discussion. The discussion will serve as inspiration to help participants think about how they can support the implementation of GOC (or other concepts) starting from their individual context and position & to see how initiatives at different levels also mutually reinforce each other. How are you going to summarize the take home messages? Following the group discussion, four junior GOC researchers will report on the lessons they have learned throughout the workshop, using the rainbow model as a guide. They will reflect on how the group discussions resonate with their own research and how the group’s reflection created perspectives for future research and implementation work both at the local and at the international level.
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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 | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".