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Record W4408295649 · doi:10.1080/02703181.2025.2473920

Evaluation of a New Advanced Physiotherapy Practice Model of Care for Patients in a Geriatric Pain Management Clinic: A Prospective Observational Study

2025· article· en· W4408295649 on OpenAlexaffabout
Eveline Matifat, François Dubé, Kadija Perreault, Amélie Kechichian, Tatiana Vukobrat, Simon Lafrance, Lisa C. Carlesso, David Lussier, F. Desmeules

Bibliographic record

VenuePhysical & Occupational Therapy In Geriatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster UniversityCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanHôpital Maisonneuve-RosemontCentre for Interdisciplinary Research in RehabilitationCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité LavalUniversité de Montréal
Fundersnot available
KeywordsObservational studyMedicinePhysical therapyPain managementPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Introduction New models of care (MOC) involving physiotherapists as first-contact practitioners in advanced practice physiotherapy (APP) roles aim to improve access and quality of care. The aim of this study was to describe a novel APP MOC in a geriatric pain clinic, regarding wait and care offered as well as patients’ pain, disability, and satisfaction.Methods This observational study recruited 65 participants from a geriatric pain clinic in Montréal (Canada). Several MOC related variables were collected and compared with usual physician care.Results Participants had significantly reduced wait times compared to physician care (55.9 days, 95%CI: 30.4-81.3). At 3-month follow-up, participants reported reduced pain severity and interference (mean reduction of 0.6/10, 95% CI: 0.1-1.1 and 0.7/10, 95% CI: 0.03-1.4). High satisfaction with APP care was reported (VSQ-9: 8.9/10, SD ± 3.6).Conclusion Initial data from this MOC reports reduced wait times and high satisfaction while improvements in clinical outcomes were observed.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.068
GPT teacher head0.451
Teacher spread0.383 · 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 designObservational
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

Citations1
Published2025
Admission routes2
Has abstractyes

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