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Record W4403576069 · doi:10.1145/3643834.3661506

Exploring Using Personalised Comics for Healthcare Communication for Patients Living With Hemodialysis

2024· article· en· W4403576069 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDesigning Interactive Systems Conference · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsSimon Fraser UniversityUniversity of Calgary
Fundersnot available
KeywordsComicsHealth careHemodialysisComputer scienceMedicineArtificial intelligenceInternal medicineEconomic growthEconomics

Abstract

fetched live from OpenAlex

Through co-design with patients undergoing hemodialysis and their healthcare professionals, we worked towards discovering how to create a personalised, welcoming, yet quick and accurate method for medical instruction communication. Exploring possibilities of meeting the widely differing goals of patients and their healthcare professionals led to designing a personalise-able method for creating comics. Through ongoing discussions during the comic creation process, we explored variations in comic styles and personalisation factors such as choosing and modifying the appearance of the comic personalities, the settings, the central topics, and word usage to create the comics. Interest in using the approach that supports the creation of medical comics was high among patients and healthcare professionals. Rich feedback was obtained about information to be included and future direction for such medical comic creation support. We reflect on lessons learned during co-design with healthcare givers and patients.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.248
GPT teacher head0.306
Teacher spread0.057 · 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