Service Integration Across Sectors in Europe: Literature and Practice
Bibliographic record
Abstract
INTRODUCTION: To meet the needs of vulnerable people, the integration of services across different sectors is important. This paper presents a preliminary review of service integration across sectors in Europe. Examples of service integration between social services, health, employment and/or education were studied. A further aim of the study was to improve conceptual clarity regarding service integration across sectors, using Minkman's Developmental Model for Integrated Care (DMIC) as an analytical framework. METHODS: The study methods comprised a literature review (34 articles) and a survey of practice examples across Europe (44 practices). This paper is based on a more comprehensive study published in 2016. RESULTS: The study demonstrates that although the focus of integration across sectors is often on social services and health care, other arrangements are also frequently in place. The review shows that integration may be either tailored to a particular target group or designed for communities in general. Although systems to monitor and evaluate social service integration are often present, they are not yet fully developed. The study also highlights the importance of good leadership and organizational support in integrated service delivery. DISCUSSION: The study shows that the DMIC can work as a conceptual framework for the analysis of service integration across sectors. However, as this is an exploratory study, further in-depth case studies are required to deepen our understanding of the processes involved in service integration across sectors.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".