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Record W3042547190 · doi:10.1136/bmj.m2254

Illustrating your research: design basics for junior clinicians and scientists

2020· article· en· W3042547190 on OpenAlexaff
Sarah Nersesian, Natasha Vitkin, Stephanie R. Grantham, Sheryl Bourgaize

Bibliographic record

VenueBMJ · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsWilfrid Laurier UniversitySimon Fraser UniversityDalhousie University
Fundersnot available
KeywordsComputer scienceData scienceMedical educationEngineering ethicsMedicineEngineering

Abstract

fetched live from OpenAlex

Communication in the fields of science, technology, engineering, and mathematics has historically been dominated by text based mediums. Scientific articles, textbooks, reviews, conference proceedings, and posters rely heavily on text to communicate scientific findings. Our current method of communication is framed primarily for those who conduct research, and we generally make little use of visual aids, which are arguably more effective.1 However, scientists and clinicians may struggle to translate scientific data into clear and informative graphics. As a group of biomedical postsecondary students and scientific illustrators interested in graphic design, we have consolidated and summarised the eight steps we use for creating eye-catching illustrations. These steps are intended as a practical resource for junior clinicians and scientists to use when creating scientific graphics, including manuscript figures, scientific poster presentations, and slides for oral presentations. Most people interested in finding information now use online resources. The internet is full of relevant search results, so being able to capture an audience’s attention is crucial for knowledge translation. Some high impact journals, including Cell , now feature and request graphical abstracts, creating a prime opportunity to incorporate visual communication into scientific data presentation.2 Data suggest that clinicians and medical trainees respond more favourably to visual communication methods such as infographics rather than to traditional text based information. For trainees and junior clinicians, designing effective and clear visuals improves communication, knowledge dissemination, and application to real-life scenarios. Visual illustrations are more memorable than words, as stated in the “picture superiority effect.” Several hypotheses have been developed as to why visuals are more memorable.3 Catching readers’ attention is especially crucial when sharing scientific pieces on social media platforms, a key part of knowledge dissemination in today’s society. Scientific illustrations on social media are more accessible than text-heavy articles; a thorough illustration enables …

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.837
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.965
GPT teacher head0.659
Teacher spread0.306 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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

Citations7
Published2020
Admission routes1
Has abstractyes

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