Comparing (and learning from) integrated care initiatives: an analytical framework
Bibliographic record
Abstract
Purpose – Care integration has been the hallmark of most proposed solutions to current and prospective challenges of health systems. However, it is an imprecise umbrella term encompassing heterogeneous models and little substantive knowledge exists on the basic mechanisms leading to positive outcomes. This study aims to address this gap by identifying the environmental conditions and the configurations of factors associated with service delivery success in integrated care initiatives. Design/methodology/approach – On the basis of an extensive literature review, an analytical framework aimed at structuring and interpreting the relations between contextual, cultural and organizational factors and the outcomes of integrated care initiatives is proposed. The framework is applied to four successful cases of care integration in the USA, Canada, Italy and Switzerland. Findings – The results suggest that positive outcomes mainly depend on the correct matching of macro-level factors with a balanced mix of operating means at the micro-level, rather than on the intense focus on any one element of the framework. Research limitations/implications – The analysis infers, from a small-purposive sample, that successful initiatives are a matter of appropriate configuration of contextual, organizational and technical factors. Generalizability of results would benefit from additional international cases and using the framework on decentralized health systems. Originality/value – This framework can guide future research efforts in the field as it is adaptable and relatively easy to operationalize. It can also be a useful tool for practitioners and policy-makers, to bring structure and reduce the complexity of efforts aimed to design, evaluate and improve integrated care initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".