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Record W2894606100 · doi:10.1080/13548506.2018.1524152

You get used to it, or do you: symptom length predicts less fibromyalgia physical impairment, but only for those with above-average self-efficacy

2018· article· en· W2894606100 on OpenAlexaboutno aff
Charles Van Liew, Gabriel A. León, Mikayla Neese, Terry A. Cronan

Bibliographic record

VenuePsychology Health & Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of Health
KeywordsFibromyalgiaDepression (economics)Physical therapyMedicinePsychologyClinical psychology

Abstract

fetched live from OpenAlex

To determine whether the effects of symptom duration on fibromyalgia physical impairment are moderated by symptom self-efficacy, data from 572 female participants, who were members of a large health maintenance organization and had a diagnosis of fibromyalgia syndrome (FMS) were assessed. Age, symptom duration, history of physical, sexual, and emotional abuse, fibromyalgia-specific self-efficacy (Arthritis Self-Efficacy Scale adapted for FMS [ASES]), depression (Centers for Epidemiological Studies Depression Scale [CES-D]), fibromyalgia physical impairment (Fibromyalgia Impact Questionnaire [FIQ]), and pain (McGill Present Pain Index [PPI]) were measured five times across 18 months. Linear regressions were performed to predict baseline FIQ and PPI cross-sectionally. Of primary interest was a hypothesized interaction between ASES and symptom duration, which was significant in relation to FIQ but not PPI. Multilevel mixed models were performed to determine whether the same pattern existed longitudinally controlling for baseline symptom duration as an effect of time and ASES. The interaction was significant in the models for both FIQ and PPI. These results suggest that the effects of age and symptom duration on FMS are unique, and that self-efficacy plays a crucial role in moderating disease course (measured by symptom duration or time) in FMS.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.419
Teacher spread0.357 · 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.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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