Archetypes of integrated care
Bibliographic record
Abstract
Purpose A synthesis of integrated care models classified by their aims and central characteristics does not yet exist. We present a collection of five “archetypes” of integrated care, defined by their aims, to facilitate model comparison and dialogue. Design/methodology/approach We used a purposive literature search and expert consultation strategy to generate five archetypes. Data were extracted from included articles to describe the characteristics and defining features of integrated care models. Findings A total of 25 examples of integrated care models (41 papers) were included to generate five archetypes of integrated care. The five archetypes defined include: (1) whole population models, (2) life stage models, (3) disease-focused models, (4) identity group-based models and (5) equity-focused models. Research limitations/implications The five presented archetypes offer a conceptual framework for academics, health system decision makers and patients, families, and communities seeking to develop, adapt, investigate or evaluate models of integrated care. Originality/value Two cross-cutting themes were identified, including (1) minimal reporting of patient, caregiver and community engagement efforts in integrated care development, implementation and evaluation, and (2) the nuanced emphasis and implementation of electronic data sharing methods across archetypes, and the need for further definition of the role of these data sharing methods.
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 | 0.065 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".