MétaCan
Menu
Back to cohort
Record W2783177981 · doi:10.1017/s0266462317003695

VP110 Building Capacity In Health Technology Assessment Through Plain Language

2017· article· en· W2783177981 on OpenAlexaboutno aff
Kinneret Globerman

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2017
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsPlain languageGovernment (linguistics)JargonPublic relationsTerminologyCLARITYHealth technologyPlain EnglishConsistency (knowledge bases)Health carePublic healthPolitical scienceMedical educationMedicineBusinessNursingComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: Health Technology Assessments (HTAs) and policy papers are generally written in academic style using industry jargon — pharmaceutical, medical, or scientific terminology — with a generous use of abbreviations. Transforming technical or biomedical data into easily understandable text is a necessity and a challenge for all of us if our goal is to facilitate HTA collaboration beyond borders and integrate HTA into healthcare practice. Many countries have legislated for plain language (PL), and organizations globally are beginning to recognize how it helps in the uptake of information, whether geared to healthcare professionals and all those interested in HTA, or the lay public. METHODS: A preliminary, informal online search for legislative and supporting guidance on PL was conducted, and a query sent out to forty-eight International Network of Agencies for Health Technology Assessment (INAHTA) members. RESULTS: • The United States Plain Writing Act of 2010 has legislated that federal agencies use “clear Government communication that the public can understand and use” (1). Of the twenty-one respondents from INAHTA Listserv, seven use plain language in either their knowledge transfer tools (such as executive and research summaries, booklets and fact sheets, and patient or lay material). • The Government of Canada promotes plain language in all of its communications (2). • McMaster University's 2014 Health Forum on strengthening public and patient engagement in HTA in Ontario supported “clarity and consistency in the use of public- and patient-engagement terminology” in HTAs. • A growing number of international health-related and HTA organizations promote PL in their reports and HTAs to help with their health literacy. • Many pharmaceutical companies encourage PL communication in their writing (3). • Of the eighteen INAHTA responses received, eight reported that they use PL in their report summaries, knowledge transfer materials, and/or patient education tools. CONCLUSIONS: Adopting the practice of clear, straightforward writing and editing in all biomedical communication — including HTAs and journal articles — encourages interaction and engagement among patient, public, and healthcare stakeholders invested in HTAs, and their desire to have measured decision making based on comprehensive, informed, and easily understandable information. However, it remains to be seen if PL will be embraced by organizations worldwide. This preliminary, informal inquiry as to its use suggests that the adoption of PL by governments, HTA organizations, and the scientific community worldwide has not yet been fully embraced.

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)0.095
metaresearch head score (Gemma)0.184
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.504

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.184
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0070.016
Scholarly communication0.0210.024
Open science0.0030.031
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0700.018

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 head0.045
GPT teacher head0.467
Teacher spread0.422 · 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.

Study designNot applicable
DomainReporting
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Technology Assessment in Health CareSame topicBiomedical Ethics and RegulationFrench-language works237,207