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Record W2126609777 · doi:10.1177/2055217315585333

Adherence to behavioural interventions in multiple sclerosis: Follow-up meeting report (AD@MS-2)

2015· article· en· W2126609777 on OpenAlexaff
Christoph Heesen, Jared M. Bruce, Robert Gearing, Rona Moss‐Morris, John Weinmann, Päivi Hämäläinen, Robert W. Motl, Ulrik Dalgas, Daphne Kos, Francesco Visioli, Peter Feys, Alessandra Solari, Marcia Finlayson, Lina Eliasson, Vicki Matthews, Angeliki Bogossian, Katrin Liethmann, Sascha Köpke, Paul Bissell

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2015
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychological interventionPsychosocialPhysical activityMultiple sclerosisIntervention (counseling)PsychologyMedicineClinical psychologyGerontologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

After an initial meeting in 2013 that reviewed adherence to disease modifying therapy, the AD@MS group conducted a follow-up meeting in 2014 that examined adherence to behavioural interventions in MS (e.g. physical activity, diet, psychosocial interventions). Very few studies have studied adherence to behavioural interventions in MS. Outcomes beyond six months are lacking, as well as implementation work in the community. Psychological interventions need to overcome stigma and other barriers to facilitate initiation and maintenance of behaviour change. A focus group concentrated on physical activity and exercise as one major behavioural intervention domain in MS. The discussion revealed that patients are confronted with multiple challenges when attempting to regularly engage in physical activity. Highlighted needs for future research included an improved understanding of patients' and health experts' knowledge and attitudes towards physical activity as well as a need for longitudinal research that investigates exercise persistence.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.524
GPT teacher head0.450
Teacher spread0.074 · 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 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

Citations21
Published2015
Admission routes1
Has abstractyes

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