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Record W2032902510 · doi:10.12927/hcq.2008.19858

Leading Change in the Transformation of Arthritis Care: Development of an Inter-professional Academic-Clinical Education Training Model

2008· article· en· W2032902510 on OpenAlexaffabout
Katie Lundon, Rachel Shupak, Lorraine Sunstrum-Mann, Debbie Galet, Rayfel Schneider

Bibliographic record

VenueHealthcare Quarterly · 2008
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsScope (computer science)Health careContext (archaeology)Scope of practiceMedicineBest practiceHealthcare deliveryMedical educationNursingProfessional developmentPolitical science

Abstract

fetched live from OpenAlex

The Advanced Clinician Practitioner in Arthritis Care (ACPAC) program is a novel, competency-based, rigorously evaluated advanced clinical and academic educational program created in 2005 and hosted by St. Michael's Hospital and The Hospital for Sick Children, Toronto, Ontario. The program is offered to experienced physical and occupational therapists selected to engage in expanded scope of practice roles with the aim to provide optimal, timely and appropriate delivery of healthcare to patients with arthritis in academic, non-academic and remote community healthcare settings. The ACPAC program is offered at a critical time in the context of rapidly changing healthcare delivery, producing highly skilled advanced practitioners across Ontario central to the development of innovative models of chronic disease management in arthritis care. The processes driving change and the risks assumed thereof, as well as a description of the successes, challenges and shortcomings of the ACPAC program, are intended to be instructive to other healthcare facilities considering similar 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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.012
Scholarly communication0.0110.008
Open science0.0030.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.106
GPT teacher head0.427
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations22
Published2008
Admission routes2
Has abstractyes

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