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Record W2750928525 · doi:10.1080/14606925.2017.1352746

Health Design: Mapping current situations, envisioning next steps

2017· article· en· W2750928525 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

VenueThe Design Journal · 2017
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHealth careKnowledge managementHealth professionalsFace (sociological concept)Process managementComputer scienceRisk analysis (engineering)PsychologyMedicineBusiness

Abstract

fetched live from OpenAlex

Healthcare systems are under high pressure, from chronic disease, and aging populations with their consequent array of issues. Healthcare professionals are asked to deliver better care with fewer resources, increasing efficiency and efficacy. Simultaneously, patients expect more personalized therapies and physicians are discovering the benefits of making patients more active in their own care. This paper outlines the challenge to arrive at healthcare decisions that are based not only on data but also on the patient’s values and preferences. However, helping healthcare to face this challenge requires designers to develop new skills and competences. The paper proposes some general competences that designers need to develop to provide design solutions to satisfy users and healthcare systems’ needs.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0020.001
Open science0.0030.000
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 head0.232
GPT teacher head0.355
Teacher spread0.124 · 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