MétaCan
Menu
Back to cohort
Record W2784320835 · doi:10.1177/1460458217749882

Turning challenges into design principles: Telemonitoring systems for patients with multiple chronic conditions

2018· article· en· W2784320835 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

VenueHealth Informatics Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultiple Chronic ConditionsModular designTelemedicineChronic diseaseMedicineProcess managementComputer scienceApplied psychologyHealth carePsychologyEngineeringIntensive care medicine

Abstract

fetched live from OpenAlex

People with multiple chronic conditions often struggle with managing their health. The purpose of this research was to identify specific challenges of patients with multiple chronic conditions and to use the findings to form design principles for a telemonitoring system tailored for these patients. Semi-structured interviews with 15 patients with multiple chronic conditions and 10 clinicians were conducted to gain an understanding of their needs and preferences for a smartphone-based telemonitoring system. The interviews were analyzed using a conventional content analysis technique, resulting in six themes. Design principles developed from the themes included that the system must be modular to accommodate various combinations of conditions, reinforce a routine, consolidate record keeping, as well as provide actionable feedback to the patients. Designing an application for multiple chronic conditions is complex due to variability in patient conditions, and therefore, design principles developed in this study can help with future innovations aimed to help manage this population.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.611
Threshold uncertainty score0.593

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 communication0.0000.000
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 head0.094
GPT teacher head0.357
Teacher spread0.263 · 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