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Record W4311416712 · doi:10.1136/bmjopen-2022-062981

Supporting patient and public partners in writing lay summaries of scientific evidence in healthcare: a scoping review protocol

2022· review· en· W4311416712 on OpenAlexafffund
Sareh Zarshenas, JoAnne Mosel, Adora Chui, Samantha Seaton, Hardeep Singh, Sandra Moroz, Tayaba Khan, Heather Colquhoun

Bibliographic record

VenueBMJ Open · 2022
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsGrey literatureMedicineContext (archaeology)Public relationsProtocol (science)Public healthKnowledge translationHealth careData extractionTarget audienceMedical educationMEDLINENursingAlternative medicineKnowledge managementPolitical scienceComputer scienceBusiness

Abstract

fetched live from OpenAlex

INTRODUCTION: Despite growing interest among patient and public partners to engage in writing lay summaries, evidence is scarce regarding the availability of resources to support them. This protocol describes the process of conducting a scoping review to: (1) summarise the source, criteria and characteristics, content, format, intended target audience, patient and public involvement in preparing guidance and development processes in the available guidance for writing lay summaries; (2) contextualise the available guidance to the needs/preferences of patient and public partners and (3) create a patient and public partner-informed output to support their engagement in writing lay summaries. METHOD AND ANALYSIS: A scoping review with an integrated knowledge translation approach will be used to ensure the collaboration between patient/public partners and researchers in all steps of the review. To meet objective 1, the English language evidence within a healthcare context that provides guidance for writing lay summaries will be searched in peer-reviewed publications and grey literature. All screening and extraction steps will be performed independently by two reviewers. Extracted data will be organised by adapting the European Union's principles for summaries of clinical trials for laypersons. For objectives 2 and 3, a consultation exercise will be held with patient and public partners to review and contextualise the findings from objective 1. A directed content analysis will be used to organise the data to the needs of the public audience. Output development will follow based on the results. ETHICS AND DISSEMINATION: Ethics approval will be obtained for the consultation exercise. Our target audience will be stakeholders who engage or are interested in writing lay summaries. Our dissemination products will include a manuscript, a lay summary and an output to support patient and public partners with writing lay summaries. Findings will be published in a peer-reviewed journal and presented at relevant conferences. OPEN SCIENCE FRAMEWORK REGISTRATION: osf.io/2dvfg.

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)0.247
metaresearch head score (Gemma)0.215
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.753
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2470.215
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0130.011
Science and technology studies0.0060.008
Scholarly communication0.0100.012
Open science0.0060.010
Research integrity0.0140.012
Insufficient payload (model declined to judge)0.0730.030

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 head0.791
GPT teacher head0.679
Teacher spread0.112 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
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

Citations6
Published2022
Admission routes2
Has abstractyes

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