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Record W4311310684 · doi:10.1136/bmjopen-2022-066802

Examining interprofessional collaboration in oncogenetic service delivery models for hereditary cancers: a scoping review protocol

2022· review· en· W4311310684 on OpenAlexafffund
Maria Eugenia Espinoza Moya, Jason R. Guertin, Michel Dorval, Julie Lapointe, Karine Bouchard, Hermann Nabi, Maude Laberge

Bibliographic record

VenueBMJ Open · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsCentre intégré de santé et de services sociaux de Chaudière-AppalachesUniversité LavalCentre hospitalier universitaire de Québec
FundersFonds de recherche du QuébecMerck CanadaUniversité Laval
KeywordsMedicineCINAHLGrey literaturePsychological interventionContext (archaeology)Systematic reviewMEDLINEProtocol (science)TerminologyKnowledge managementNursingComputer scienceAlternative medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: In a context of limited genetic specialists, collaborative models have been proposed to ensure timely access to high quality oncogenetic services for individuals with inherited cancer susceptibility. Yet, extensive variability in the terminology used and lack of a clear understanding of how interprofessional collaboration is operationalised and evaluated currently constrains the development of a robust evidence base on the value of different approaches used to optimise access to these services. To fill in this knowledge gap, this scoping review aims to systematically unpack the nature and extent of collaboration proposed by these interventions, and synthesise the evidence available on their implementation, effectiveness and economic impact. METHODS AND ANALYSIS: Following the Joanna Briggs Institute guidelines for scoping reviews, a comprehensive literature search will be conducted to identify peer-reviewed and grey literature on collaborative models used for adult patients with, or at increased risk of, hereditary breast, ovarian, colorectal and prostate cancers. An initial search was developed for Medline, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Cochrane and Web of Science on 13 June 2022 and will be complemented by searches in Google and relevant websites. Documents describing either the theory of change, planning, implementation and/or evaluation of these interventions will be considered for inclusion. Results will be summarised descriptively and used to compare relevant model characteristics and synthesise evidence available on their implementation, effectiveness and economic impact. This process is expected to guide the development of a definition and typology of collaborative models in oncogenetics that could help strengthen the knowledge base on these interventions. Moreover, because we will be mapping the existing evidence on collaborative models in oncogenetics, the proposed review will help us identify areas where additional research might be needed. ETHICS AND DISSEMINATION: This research does not require ethics approval. Results from this review will be disseminated through peer-reviewed articles and conferences.

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)0.119
metaresearch head score (Gemma)0.098
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.119
Threshold uncertainty score0.632

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1190.098
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0130.015
Bibliometrics0.0240.018
Science and technology studies0.0060.006
Scholarly communication0.0100.010
Open science0.0070.009
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.0610.011

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 head0.210
GPT teacher head0.513
Teacher spread0.303 · 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
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

Citations5
Published2022
Admission routes2
Has abstractyes

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