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Record W2936347179 · doi:10.1136/lupus-2019-lsm.191

191 MyLupusGuide, a lupus-specific web interactive navigator, improves self-efficacy and activation in patients with low activation and in men

2019· article· en· W2936347179 on OpenAlexaffabout
Paul R. Fortin, Carolyn Neville, Anne‐Sophie Julien, Elham Rahme, Murray Rochon, Vinita Haroun, Évelyne Vinet, Christine Peschken, Ann E. Clarke, Janet Pope, Stephanie Keeling, Antonio Aviña-Zubieta, Douglas P. Smith, Mark Matsos, Marie Hudson, Jodie Young, Anna‐Lisa Morrison, Davy Eng, Deborah DaCosta

Bibliographic record

VenueAbstracts · 2019
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsMcMaster UniversityUniversity of OttawaWestern UniversityResearch CanadaUniversity of ManitobaMcGill UniversityUniversity of CalgaryMcGill University Health CentreUniversity of AlbertaUniversité Laval
Fundersnot available
KeywordsMedicineSystemic lupus erythematosusPopulationCoping (psychology)Physical therapyInternal medicineDiseaseClinical psychology

Abstract

fetched live from OpenAlex

Background Systemic Lupus Erythematosus (SLE) is an unpredictable multisystem chronic disease that leads to insecurity, requires life-style adaptations, work accommodations and longterm medication use. We previously reported that a web-based interactive navigator named MyLupusGuide (MLG) was well accepted by lupus patients and met with their informational needs. When used without reinforcement however MLG did not change patient activation towards self-management. We performed additional analyses to test if subgroups of patients were more likely to become activated than others in a large lupus population. Methods Population and recruitment strategy: Patients from ten lupus centers were randomized to either immediate access to MLG (NOW) or usual care (LATER). Partial cross-over occurred at three months and there was a final assessment at six months. Data collected: Demographic and socioeconomic data were collected at baseline. The 13-item Patient Activation Measure (PAM) was used to assess patient`s healthcare engagement. Higher PAM score relates to greater engagement. Additional self-reported measures for self-efficacy (Lupus Self-Efficacy Scale - LSES) and coping strategies (Coping with Health Injuries and Problems - CHIP) were obtained at baseline, 3 and 6 months. Statistical analyses: Linear mixed models were used to test the evolution of PAM over time between groups. This abstract reports on the following subanalyses: analyses of the subgroup with low PAM score at baseline and of being male or female, and analyses of other outcomes such as LSES and CHIP. Results A total of 541 of 1920 (28%) lupus patients responded at baseline, 399 at 3 months and 355 at 6 months. At baseline, mean (sd) age=50.1 (14.2) years, female=93%, Caucasian=74%, disease duration=16.9 (11.9) years and PAM score=61.1 (13.5). The following subanalyses (table 1) showed a beneficial effect of MLG on activation after three months in the subgroup of patients with low PAM at baseline, as well as for men. A significant improvement in LSES was also observed after 3 months of exposure to MLG but there was no change in CHIP. Conclusions At 3 months, access to MLG improved activation in patients with a low activation at baseline and in men. Self-efficacy also improved significantly without changes in coping strategies. The MLG is a unique web-based resource that provides reliable information for patients with lupus to assist them with disease management and lifestyle adaptations. Funding Source(s): A Knowledge-to-Action Canadian Institute for Health Research grant. Dr. Fortin holds a Canada Research Chair.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0030.000

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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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