Bibliographic record
Abstract
Integrated care's missing piece: the US experienceI recently talked to a colleague from the US who is interested in integrated care, and it got me thinking: why do we publish so few integrated care studies in our journal from the US?You may say that this is just a blind spot in this journal, whilst there are plenty of wonderful examples of integrated care initiatives in the country.However, I have been at many integrated care conferences in Europe and outside Europe and have rarely seen a strong representation at these events from the US.I know there is a lot of interest in care integration in the US by scholars and researchers.In fact, some of the foundational texts of the discipline were written by US scholars (Leutz, 1999(Leutz, , 2005)).So why is it that we see so few papers by US authors submitted to this journal, and, may I surmise, probably also to any other integrated care journal?Before embarking on possible explanations, I should probably say why I think this matters.The lack of submissions to our journal from the US is not simply a commercial concern.It is a question whose answer may yield insights into the nature of integrated care as we practice it.By better understanding why US scholars do not write papers on integrated care to the same extent as scholars from Canada, Europe, Australia or South East Asia, we may generate insights into what provides the foundation for the current formation of integrated care research.If you ask ChatGPT, you may get a succinct response to the question: Why the US is not awash with 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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".