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Record W4378782776 · doi:10.1136/bmjopen-2023-072006

Evaluation of an interprofessional primary healthcare team as a new model of primary care in Quebec: a protocol for a type 2 effectiveness-implementation hybrid study

2023· article· en· W4378782776 on OpenAlexafffundabout
Nancy Côté, Yasmine Frikha, Andrew Freeman, Sergio Cortez Ghio, Maude Laberge, Maripier Isabelle, Arnaud Duhoux, Jean‐Louis Denis, Emmanuelle Jean, Sébastien Binette

Bibliographic record

VenueBMJ Open · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversité de MontréalPublic Health Agency of CanadaCentre hospitalier de l'Université LavalUniversité Laval
FundersFonds de Recherche du Québec - SantéMinistère de la Santé et des Services sociaux
KeywordsMedicinePrimary careProtocol (science)Primary health careHealth careInterprofessional educationNursingFamily medicineMedical educationAlternative medicineEnvironmental health

Abstract

fetched live from OpenAlex

Introduction One family medicine group (FMG) in Quebec has commenced a 5-year pilot project, which is herein referred to as the Archimède model, to implement a patient-centred model based on interprofessional care and the optimal use of healthcare providers’ practice scopes. A research project will be conducted to: (1) assess this model’s effect on the FMG’s operational performance, and its users’ resource utilisation at the public health system level; (2) investigate its optimisation with respect to professional roles, interprofessional teamwork and patient-centredness and (3) document users’ experience with the model. The aim of this article is to describe the protocol that will be used for this research. Methods and analysis A hybrid implementation approach (type 2 model) will be used. We will collect both quantitative and qualitative data. Regarding the quantitative dimension, and because this is a single-unit intervention study, we will use either or both synthetic control methods and one-sample generalised linear models for analyses at the FMG level. To evaluate the broader impact of Archimède on the public health system, we will use mixed-effects models and propensity score matching methods. Regarding the qualitative research dimension, using an interpretative descriptive approach, we will document users’ experience and identify the factors that optimise professional scopes of practice, collaborative practices and patient-centredness. We will conduct individual in-depth semistructured interviews with healthcare providers, administrative staff, stakeholders involved in the Archimède model implementation and patients. Ethics and dissemination This study was approved by the Ethics Committee of the Sectoral Research in Population Health and Primary Care of the Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale (#2019-1503). The results of the investigation will be presented to the stakeholders involved in the advisory committees and at several scientific conferences. Manuscripts will be submitted to peer-reviewed journals.

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)0.184
metaresearch head score (Gemma)0.115
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 score0.954
Threshold uncertainty score0.974

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1840.115
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.005
Science and technology studies0.0060.005
Scholarly communication0.0060.004
Open science0.0070.005
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0280.003

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 head0.295
GPT teacher head0.644
Teacher spread0.349 · 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

Citations1
Published2023
Admission routes3
Has abstractyes

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