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Record W2764112758 · doi:10.1177/0308022617728679

Understanding and living with multiple sclerosis fatigue

2017· article· en· W2764112758 on OpenAlexaff
Merrill Turpin, Georgina Kerr, Hannah Gullo, Sally Bennett, Miho Asano, Marcia Finlayson

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMultiple sclerosisThematic analysisPsychologyExistentialismIdentity (music)MedicineClinical psychologyQualitative researchPsychiatry

Abstract

fetched live from OpenAlex

Introduction Fatigue substantially affects the lives of many people with multiple sclerosis. This study aimed to further our understanding of the experience of living with multiple sclerosis fatigue by exploring how people became aware of and understood their multiple sclerosis fatigue and how they accommodate it in their daily lives. Method The study used an existential approach to thematic analysis. Thirteen in-depth, semi-structured interviews with people who experienced multiple sclerosis fatigue were conducted and analysed. Results Participants developed an understanding of multiple sclerosis fatigue through gaining awareness of its effect on their lives, seeking information themselves and being informed by health professionals. Participants described how they began to understand the effect of fatigue in their lives and make decisions about how to accommodate it. They discussed the challenges associated with helping others to understand their multiple sclerosis fatigue. Conclusion Lay and expert explanations, the phenomenological notions of lived experience, self-identity and embodiment and stigma associated with invisible disability were useful concepts for understanding the results. Clinicians should consider these concepts when supporting people with multiple sclerosis fatigue to understand the effect of fatigue in their daily lives and use fatigue management strategies to make effective lifestyle changes to accommodate it.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.042
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.460
GPT teacher head0.395
Teacher spread0.064 · 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 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

Citations18
Published2017
Admission routes1
Has abstractyes

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