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Record W2786043366 · doi:10.1177/1049732317753584

The Experience of Persons With Multiple Sclerosis Using MS INFoRm: An Interactive Fatigue Management Resource

2018· article· en· W2786043366 on OpenAlexaff
Julie Pétrin, Nadine Akbar, Karen Turpin, Penelope Smyth, Marcia Finlayson

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of AlbertaQueen's University
Fundersnot available
KeywordsMultiple sclerosisPsychological interventionQuality of life (healthcare)Resource (disambiguation)PsychologySelf-managementMedicineClinical psychologyNursingPsychiatryComputer science

Abstract

fetched live from OpenAlex

We aimed to understand participants' experiences with a self-guided fatigue management resource, Multiple Sclerosis: An Interactive Fatigue Management Resource ( MS INFoRm), and the extent to which they found its contents relevant and useful to their daily lives. We recruited 35 persons with MS experiencing mild to moderate fatigue, provided them with MS INFoRm, and then conducted semistructured interviews 3 weeks and 3 months after they received the resource. Interpretive description guided the analysis process. Findings indicate that participants' experience of using MS INFoRm could be understood as a process of change, influenced by their initial reactions to the resource. They reported experiencing a shift in knowledge, expectations, and behaviors with respect to fatigue self-management. These shifts led to multiple positive outcomes, including increased levels of self-confidence and improved quality of life. These findings suggest that MS INFoRm may have a place in the continuum of fatigue management interventions for people with MS.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.631
GPT teacher head0.590
Teacher spread0.041 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2018
Admission routes1
Has abstractyes

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