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Record W2027321632 · doi:10.3138/ptc.2011-27

Physiotherapy Models of Service Delivery, Staffing, and Caseloads: A Profile of Level I Trauma Centres across Canada

2012· article· en· W2027321632 on OpenAlexaffvenueabout
Megan Fisher, Martha N. Aristone, Katrina Young, Laurie E. Waechter, Michel D. Landry, Leslie A. Taylor, Nicole S. Cooper

Bibliographic record

VenuePhysiotherapy Canada · 2012
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsStaffingService delivery frameworkPhysical therapyMedicineService (business)NursingMedical emergencyBusiness

Abstract

fetched live from OpenAlex

PURPOSE: To examine and describe physiotherapy models of service delivery, staffing, and caseloads in Level I trauma centres across Canada. METHODS: A telephone questionnaire was administered to one experienced trauma physiotherapist at each of the 19 Level I trauma centres in Canada. Quantitative data were analyzed descriptively for national trends. RESULTS: Data were collected from all 19 centres (100%), 89% of which provided physiotherapy services 5 days per week with priority weekend coverage. Physiotherapist assistants (PTAs) were employed by 89% of centres and were used across the continuum of care. Centres with PTAs appear to be more likely to provide patients with additional daily treatment. Departmental organizational structures were the most common (41%) and were associated with higher caseloads. Higher caseloads also appear to be linked with having less than 10 years of experience as a physiotherapist. CONCLUSIONS: Variations exist between centres with respect to the delivery of physiotherapy services. These variations may result from differences in province-specific legislation, differences in funding structure, and the lack of evidence-informed guidelines. Future research is needed to establish optimal models of physiotherapy services that are cost-effective and provide best patient care.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.298
Teacher spread0.268 · 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 designBench or experimental
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

Citations7
Published2012
Admission routes3
Has abstractyes

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