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Record W4307956202 · doi:10.1016/j.hfh.2022.100025

Designing technologies for self-care: Describing the lived experiences of individuals with rheumatoid arthritis

2022· article· en· W4307956202 on OpenAlexafffund
Marina Wada, James R. Wallace

Bibliographic record

VenueHuman Factors in Healthcare · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychosocialCoping (psychology)Thematic analysisMedicineSelf-managementPsychologyRheumatoid arthritisAgency (philosophy)DiseaseGerontologyClinical psychologyQualitative researchPsychiatryComputer science

Abstract

fetched live from OpenAlex

Self-care of a chronic illness is a lifelong management process that includes taking medications, monitoring symptoms, and coping with emotional and lifestyle changes. Human-Computer Interaction (HCI) research has often responded to these needs by developing technologies that help an individual quantify aspects of their chronic illness, like daily pain, flare ups, or personal behaviours like diet and exercise. But this quantification fails to account for the ongoing needs of understanding ones disease and maintaining a balanced lifestyle. To understand these needs, we interviewed 12 people about their lived experience with Rheumatoid Arthritis (RA). We performed a thematic analysis of collected data and identified three types of support currently lacking for RA: (1) psychosocial care (2) patient agency, and (3) lifestyle adaptations. Our results highlight the need to support long term uncertainty while living with a chronic illness and identify needs the HCI community should consider when developing self-care technologies.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.340

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 communication0.0000.000
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 head0.086
GPT teacher head0.307
Teacher spread0.221 · 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 designQualitative
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
Published2022
Admission routes2
Has abstractyes

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