Co-production: Strategic Lever for Dehospitalization and Redefinition of Organizational Structures in Healthcare
Bibliographic record
Abstract
Objective - The explosion of the covid-19 pandemic has led to the need for all world governments to redefine the way in which they provide health services. This is particularly true for Italy, as the hospital-centric emergency response model adopted in the first pandemic wave to contain and combat the health crisis and to treat affected patients proved ineffective. The purpose of this work is to highlight how the model of co-production, based on the enhancement of territorial services and 3T strategy (Tracing, Testing e Treating), may be the most appropriate paradigm to address the emergency. Methodology - Through an in-depth analysis of co-production model emerging from the scientific literature, we highlight the critical success and enabling factors that make the model applicable in healthcare in the Covid-19 emergency context and the advantages of the paradigm. Findings - The nature of the emergency could incentivize citizens to spontaneously participate in co-production activities, provided a favorable social and legislative context. Co-production could allow to implement the 3T strategy effectively and efficiently.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".