Value Co-creation in the Health Service Ecosystems: The Enabling Role of Institutional Arrangements
Bibliographic record
Abstract
The health care service system is currently undergoing a profound revolution that has put the patient at the core of health outcome co-creation. Patient-centered care could be associated with Service Dominant Logic that looks at co-creation process as a dynamic resources’ integration between actors. From this standpoint, the need for a broader vision of value creation process towards a service ecosystem perspective is emerging. This paper includes an overview of the scientific literature and reports on a narrative case study analysis concerning the "International Consortium for Health Outcomes Measurement" in an attempt to nourish the debate on the different ways that multiple actors can collaboratively shape a health service ecosystem. Findings reveal that co-creation practices, involving multiple actors who belong to different ecosystem levels, led to mutual adjustments and to on-going shared changes. These processes directly influenced health outcome creation, which is reframed in light of patients’ needs, expectations, and experiences. Therefore, patients are assuming the role of health outcome “co-creator”, interacting with all other ecosystems actors (e.g. physicians, institutions, NGOs, health managers, ICTs providers etc.). This study represents a first and preliminary attempt to investigate a real example of dynamic resources’ exchange, based on the contribution of multiple interacting actors and on the role of interdepend and interacting institutions in value practices.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".