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Record W2981212411 · doi:10.1136/bmjopen-2019-029542

Factors associated with patient-reported likelihood of using online self-care interventions: a Scleroderma Patient-centered Intervention Network (SPIN) cohort study

2019· article· en· W2981212411 on OpenAlexafffundabout
Linda Kwakkenbos, Julie Cumin, Marie‐Eve Carrier, Susan J. Bartlett, Vanessa L. Malcarne, Luc Mouthon, Warren R. Nielson, François Rannou, Joep Welling, Brett D. Thombs

Bibliographic record

VenueBMJ Open · 2019
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsSt Joseph's Health CareMcGill UniversityMcGill University Health CentreLawson Health Research InstituteJewish General Hospital
FundersLady Davis Institute for Medical ResearchCanadian Institutes of Health ResearchScleroderma Association of British ColumbiaScleroderma Society of OntarioJewish General HospitalNational Center for Advancing Translational SciencesArthritis SocietyMcGill University
KeywordsMedicinePsychological interventionIntervention (counseling)CohortCohort studyPhysical therapyFamily medicinePsychiatryPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: The Scleroderma Patient-centered Intervention Network (SPIN) Cohort uses the cohort multiple randomised controlled trial design to embed trials of online self-care interventions for people living with systemic sclerosis (SSc; scleroderma). To offer interventions to patients interested in using them, participants complete signalling items that query about the likelihood that patients would agree to participate in nine different hypothetical online programmes addressing common SSc-related problems. It is not known what factors influence patient-reported interest in participating in a particular online intervention and if intervention-specific signalling questions provide unique information or replicate broader characteristics, such as overall willingness to participate or self-efficacy. This study assessed factors that explain responses to intervention-specific signalling items. DESIGN: Cross-sectional survey. SETTING: SPIN Cohort participants enrolled at 42 centres from Canada, the USA, the UK, France, Spain and Mexico who completed study questionnaires from March 2014 to January 2018 were included. MEASURES: Demographic and disease characteristics, self-efficacy and symptoms related to each specific intervention were completed in addition to signalling items. General likelihood of using interventions was calculating by taking the mean score of the remaining signalling questions. PARTICIPANTS: 1060 participants with complete baseline data were included in the analyses. RESULTS: For all individual signalling questions, controlling for other variables, the mean of the remaining signalling questions was the strongest predictor (standardised regression coefficient β from 0.61 (sleep) to 0.80 (self-management)). Smaller, but statistically significant, associations were found with the symptom associated with the respective signalling question and with general self-efficacy for 7 of 9 signalling questions. CONCLUSIONS: The main factor associated with patients' interest in participating in a disease-specific online self-care intervention is their general interest in participating in online interventions. Factors that may influence this general interest should be explored and taken into consideration when inviting patients to try online interventions.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.117
GPT teacher head0.379
Teacher spread0.262 · 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 designObservational
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

Citations12
Published2019
Admission routes3
Has abstractyes

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