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Record W4411884037 · doi:10.3899/jrheum.2025-0314.99

The Advanced Clinician Practitioner in Arthritis Care Contributions to the Scientific Literature: A Scoping Review

2025· review· en· W4411884037 on OpenAlexaffvenue
J. Lynwood Herrington, Lisa Caldana, Kristi Whitney, Daeria O. Lawson, Jo‐Anne Petropoulos, Laura Passalent

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsUniversity Health NetworkUniversity of TorontoSickKids FoundationHamilton Health SciencesHospital for Sick ChildrenMcMaster University
Fundersnot available
KeywordsMedicineMEDLINEFamily medicineSystematic reviewHealth careDescriptive statisticsAlternative medicineMeta-analysisEnglish languagePhysical therapyMedical educationInternal medicinePathologyPsychology

Abstract

fetched live from OpenAlex

Objectives The Advanced Clinician Practitioner in Arthritis Care (ACPAC) program prepares experienced health professionals (physiotherapists, occupational therapists, nurses and chiropractors) in the advanced assessment and management of rheumatic and musculoskeletal disease. Since the inception of the program, graduates have participated in health research and program evaluation. The aim of this study is to describe the peer-reviewed literature regarding ACPAC clinicians, the ACPAC program and literature authored by ACPAC graduates and/or program leadership. Methods A PRISMA-compliant scoping review was conducted to explore the above aim. Ovid Embase, Ovid MEDLINE and Web of Science were searched from June 2006 (first ACPAC program cohort) to June 2023. English-language studies of any design were included if they mentioned the ACPAC program, ACPAC clinicians and/or were authored by an ACPAC graduate and/or program leadership. Four reviewers (JH, LC, KW, LP) independently screened titles and abstracts according to pre-established criteria. Next, reviewer pairs conducted full-text reviews and extracted data from the selected publications. Conflicts were resolved by consensus among the investigative team. Descriptive statistics characterized the literature according to authorship, publication type, and study design. Articles were evaluated for factors of health equity using the PROGRESS-Plus framework. Results Of 5987 articles retrieved and screened, 106 were included for analysis. Of these, 85.9% were authored by an ACPAC graduate. 42.4% of the articles mentioned the word “ACPAC”. Most articles were published in rheumatology/arthritis focused journals (37.7%), followed by physiotherapy journals (14.2%). First authorship was observed in 21.5% of included articles, followed by 72% where ACPAC graduates were contributing author(s). Most ACPAC authors were affiliated with a healthcare institution (78.9%). Journal impact factor (JIF) ranged from 0.297 (Physiotherapy Quarterly) to 96.2 (New England Journal of Medicine), with an average JIF of 4.55 (SD 12.67). Over the data capture period, the year 2018 had the most publications per annum (n=18), with an average of 5-6 articles published per year. Observational (44.4%) and qualitative (25.6%) study designs were most frequently published. Rheumatic diseases (56.5%) and orthopedics (21.7%) were the most common areas of study. Evaluation of health equity factors revealed little mention of data collection related to race/ethnicity/culture/language, religion, social capital or socioeconomic status. Conclusion This scoping review demonstrates substantial contributions to the scientific literature by ACPAC graduates and program leadership in the study of rheumatic disease and orthopedics. Future consideration for ACPAC-related scientific inquiry includes expansion of study design (eg, controlled studies, systematic reviews) with conscious inclusion of health equity in study methodology.

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)0.062
metaresearch head score (Gemma)0.201
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.062
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.201
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0620.053
Science and technology studies0.0030.003
Scholarly communication0.0100.011
Open science0.0030.005
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.402
Teacher spread0.389 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2025
Admission routes2
Has abstractyes

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