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Record W2606600727 · doi:10.3899/jrheum.170076

Another Nasty Effect of Opioids: Attenuating the Benefits of Motivational Interviewing in Fibromyalgia?

2017· letter· en· W2606600727 on OpenAlexaffvenueabout
Mary‐Ann Fitzcharles, Yoram Shir

Bibliographic record

VenueThe Journal of Rheumatology · 2017
Typeletter
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMotivational interviewingMedicinePsychological interventionIntervention (counseling)FibromyalgiaPopularityEmpowermentBehavior changePhysical therapyNursingPsychology

Abstract

fetched live from OpenAlex

The reasons why fibromyalgia (FM) patients fail to adhere to medical advice are complex and multifactorial, with consequent challenge to effective medical care. While adherence to medication treatments is generally poor, adherence to lifestyle intervention recommendations may be even poorer1,2. FM is a condition without cure at this time and with symptoms expected to be lifelong for most. Current guidelines recommend regular physical activity as a critical and necessary treatment strategy for FM; both short-term and longterm benefits of exercise have been demonstrated, emphasizing the need to promote adherence to these recommendations3,4,5,6. Nevertheless, continued practice of exercise by FM patients remains poor7. Strategies proposed to maintain adherence include patient education and empowerment, encouragement to participate in an activity that is pleasurable and convenient, and psychological interventions that may include cognitive behavioral training or motivational interviewing (MI)8. MI is a patient-centered technique that aims to facilitate patient involvement in maintaining good, health-centered behavior patterns. This psychological strategy, developed in the 1980s, was initially used in the management of substance abuse disorders9. As the popularity of MI increased, along with the appreciation of the importance of active patient/person participation in health care, it was used in various other health-related settings such as prevention programs for smoking and alcohol use in youth, and then to a wider application to promote adherence to treatment recommendations in diverse diseases. The objective of MI is to sufficiently motivate a person to develop a closed loop of achievement, whereby a person is able to set and achieve goals that will continue to provide personal motivation by generating a self-sustaining loop. MI may be delivered in various formats, including face-to-face encounter, telephone contact or online groups, as well … Address correspondence to Dr. M.A. Fitzcharles, Montreal General Hospital, 1650 Cedar Ave., Montreal, Quebec H3G 1A4, Canada. E-mail: mary-ann.fitzcharles{at}muhc.mcgill.ca

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.032
GPT teacher head0.308
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2017
Admission routes3
Has abstractyes

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