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Record W3004733801 · doi:10.1136/bmjopen-2019-034526

Protocol for the process and feasibility evaluations of a new model of primary care service delivery for managing pain and function in patients with knee osteoarthritis (PARTNER) using a mixed methods approach

2020· article· en· W3004733801 on OpenAlexaff
Jocelyn L. Bowden, Thorlene Egerton, Rana S. Hinman, Kim L. Bennell, Andrew M. Briggs, Stephen Bunker, Jessica Kasza, Simon French, Marie Pirotta, Deborah Schofield, Nicholas Zwar, David J. Hunter

Bibliographic record

VenueBMJ Open · 2020
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsImpact
FundersMedical Research CouncilInstitut National Du CancerBupa Health FoundationMedibank Better Health FoundationMonash UniversityAustralian GovernmentNational Health and Medical Research CouncilPfizerArthritis AustraliaEli Lilly and Company
KeywordsMedicineThematic analysisPsychological interventionProtocol (science)Multidisciplinary approachIntervention (counseling)NursingService delivery frameworkFidelityService (business)Process managementPhysical therapyQualitative researchAlternative medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: This protocol outlines the rationale, design and methods for the process and feasibility evaluations of the primary care management on knee pain and function in patients with knee osteoarthritis (PARTNER) study. PARTNER is a randomised controlled trial to evaluate a new model of service delivery (the PARTNER model) against 'usual care'. PARTNER is designed to encourage greater uptake of key evidence-based non-surgical treatments for knee osteoarthritis (OA) in primary care. The intervention supports general practitioners (GPs) to gain an understanding of the best management options available through online professional development. Their patients receive telephone advice and support for OA management by a centralised, multidisciplinary 'Care Support Team'. We will conduct concurrent process and feasibility evaluations to understand the implementation of this new complex health intervention, identify issues for consideration when interpreting the effectiveness outcomes and develop recommendations for future implementation, cost effectiveness and scalability. METHODS AND ANALYSIS: The UK Medical Research Council Framework for undertaking a process evaluation of complex interventions and the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) frameworks inform the design of these evaluations. We use a mixed-methods approach including analysis of survey data, administrative records, consultation records and semistructured interviews with GPs and their enrolled patients. The analysis will examine fidelity and dose of the intervention, observations of trial setup and implementation and the quality of the care provided. We will also examine details of 'usual care'. The semistructured interviews will be analysed using thematic and content analysis to draw out themes around implementation and acceptability of the model. ETHICS AND DISSEMINATION: The primary and substudy protocols have been approved by the Human Research Ethics Committee of The University of Sydney (2016/959 and 2019/503). Our findings will be disseminated to national and international partners and stakeholders, who will also assist with wider dissemination of our results across all levels of healthcare. Specific findings will be disseminated via peer-reviewed journals and conferences, and via training for healthcare professionals delivering OA management programmes. This evaluation is crucial to explaining the PARTNER study results, and will be used to determine the feasibility of rolling-out the intervention in an Australian healthcare context. TRIAL REGISTRATION NUMBER: ACTRN12617001595303; Pre-results.

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)0.163
metaresearch head score (Gemma)0.177
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.184
Threshold uncertainty score0.862

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1630.177
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.006
Science and technology studies0.0070.006
Scholarly communication0.0060.005
Open science0.0060.006
Research integrity0.0080.016
Insufficient payload (model declined to judge)0.1840.039

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 head0.181
GPT teacher head0.429
Teacher spread0.248 · 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
GenreProtocol

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

Citations8
Published2020
Admission routes1
Has abstractyes

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