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Record W4399566216 · doi:10.1097/phm.0000000000002576

Expanding Integrated Physiatric Care

2024· article· en· W4399566216 on OpenAlexaff
Christian D. Fortin, Lawrence R. Robinson

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2024
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineRehabilitationPhysical therapyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.315
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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