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Predictors of Adherence to Treatment in Women With Fibromyalgia

2006· article· en· W2062834069 on OpenAlexaff
Patricia L. Dobkin, Aurelio Sita, Maida Sewitch

Bibliographic record

VenueClinical Journal of Pain · 2006
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsMedicineFibromyalgiaPsychosocialPhysical therapyCohortDistressDepression (economics)Generalized estimating equationPsychological distressMedication adherenceInternal medicinePsychiatryClinical psychologyAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVES: The goal of this study was to identify predictors of general and medication adherence in women with fibromyalgia (FM). METHODS: Participants were 142 women recruited from tertiary care hospitals or the community and 10 rheumatologists. Participants' demographic, clinical, and psychosocial characteristics, as well as patient-physician discordance, were assessed at the index visit. Adherence was assessed 6 months later. Multivariable generalized estimating equations were used to identify predictors of general adherence and adherence to medication. RESULTS: The average age of participants was 50.9 years (SD=10.2) and the median duration of FM was 32 months. Participants reported extensive use of health services and medications. The mean score for general adherence was 61.0 (SD=22.4; range 0-100) and 52.9% of the cohort reported at least one form of behavior reflecting nonadherence to medications. More general adherence was significantly predicted by lower patient-physician discordance on patient well-being and lower patient psychological distress. Medication adherence was significantly predicted by higher affective pain and lower patient psychological distress. CONCLUSIONS: Adherence is influenced by both clinical (patient-physician discordance and pain) and psychological (distress) factors in women with FM. Improvements in these domains may improve adherence in FM.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.037
GPT teacher head0.355
Teacher spread0.318 · 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

Citations83
Published2006
Admission routes1
Has abstractyes

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