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Record W2088540292 · doi:10.1109/vast.2014.7042529

The care and condition monitor: Designing a tablet based tool for visualizing informal qualitative healthcare data

2014· article· en· W2088540292 on OpenAlexafffund
Anne Stevens, Hudson Pridham, Steve Szigeti, Sara Diamond, Bhuvaneswari Arunchalan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsOntario College of Art and Design
FundersMitacs
KeywordsVisualizationScope (computer science)Health careData visualizationStructuringComputer scienceComprehensionVisual analyticsAnalyticsQualitative researchHuman–computer interactionData scienceKnowledge managementMultimediaData mining

Abstract

fetched live from OpenAlex

Visual analytic tools, combined with social networks and mobile platforms, make it possible to create multi-dimensional, holistic pictures of people's health care and condition and expand the scope of information addressed in medical records. The Care and Condition Monitor (CCM) is a tablet-based, networked visual analytics tool for collecting, structuring and analyzing informal and qualitative healthcare data and visualizing it in a circular format. It illustrates how social communication within teams of caregivers enables capturing of longitudinal informal data that can (a) result in rich and meaningful information visualizations, (b) improve comprehension of healthcare data and changes in condition over time, and (c) support medical decision making.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.075
GPT teacher head0.416
Teacher spread0.342 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2014
Admission routes2
Has abstractyes

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