Bibliographic record
Abstract
ABSTRACT: Siloed and episodic care delivery is often not equitable, high quality, or sustainable. Transitioning from separate care settings, with potentially divergent care models, to an integrated care model is not always straightforward. Some experiences in expanding collaborative care between physiatrists and other healthcare providers for a variety of patient populations and care settings within a university physical medicine and rehabilitation division are shared as a means to inspire the uptake of care integration initiatives more broadly within the specialty. After an initial survey of care integration across multiple clinical sites, the university division highlighted successful integrated care models, discussed integrated care models at every divisional retreat, reached out to clinicians in other specialties to collaboratively explore expansion, developed a "one-pager" on what physiatrists do, and invited collaborative specialists from integrated clinics to physical medicine and rehabilitation national and/or international meetings. Since 2019, divisional activity in integrated care has grown and evolved substantially. Future work will focus on further expansion of integrated clinical care, scholarly evaluation of integrated care models, expansion of academic activity resulting from integration, and advocacy to healthcare providers, hospital administrators, and health system funders about the potential value of care integration in improving rehabilitation outcomes.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".