Navigating a Decade of Integrated Care Research in the International Journal of Integrated Care: How Far Have We Come?
Bibliographic record
Abstract
Introduction: The scope of integrated care has evolved and broadened the past decades from specialist pathways to incorporate a more holistic approach. To identify such trends in evidence and knowledge, we analysed published papers in the International Journal of Integrated Care over a 10-year period. Methods: From an initial set of 5.075 IJIC papers (2012-2022), 508 articles were selected after excluding several categories such as poster and conference abstracts. As no existing theoretical framework seemed to fit our study aim, we chose to focus specifically on two important areas in integrated care that are known for development; impact measurement and co-production in research. Results: There was an overall growth of published papers in the journal. The papers predominantly feature contributions from Western regions, including Europe, the Western Pacific and the Americas. Results regarding impact measurement showed no clear overarching pattern over time. Engaging the target population as co-producers in the studies is still low (<5%). Conclusions: Although the number of papers increased pointing towards more attention for integrated care, we could not identify any significant growth or advancement in the two crucial areas of co-production and impact measurement in integrated care research. These gaps need to be addressed accordingly in both practice and research.
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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 | 0.101 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.028 | 0.032 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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, 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".