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Record W3153302795 · doi:10.5507/euj.2020.012

A Qualitative Exploration of Factors Influencing Physical Activity Behaviour for Individuals with Parkinson's Disease Using the Social Ecological Model

2021· article· en· W3153302795 on OpenAlexaff
Bradley MacCosham, Evan Webb, Wahid Hamidi, Jessica Oey, François Gravelle

Bibliographic record

VenueEuropean Journal of Adapted Physical Activity · 2021
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsParkinson's diseaseEcologyDiseasePsychologySocial ecological modelBiologyMedicinePathology

Abstract

fetched live from OpenAlex

Physical activity (PA) can benefit individuals with Parkinson’s disease (PD), however, many individuals tend to be sedentary. This qualitative study explored factors influencing PA behaviour for individuals with PD using the social ecological model. Twelve individuals with PD took part in semi-structured interviews. Data were thematically analysed. Results suggest that individuals with PD experience several constraining and facilitating factors to PA behaviour. Intrapersonal constraints revolved around uncertainties that PA is beneficial for individuals with PD, a lack of interest in available PA programs, and disease-specific issues whereas intrapersonal facilitators included prior experiences of enjoyment in PA, seeing improvements, and wanting to maintain independence. Interpersonal constraints related to lacking social support and perceived stigma whereas interpersonal facilitators were, passionate PA program staff, and being active with similar others. Environmental constraints pertained to PA programs failing to adapt program activities, lack of time, and transportation accommodations, whereas environmental facilitators were exposure to non-traditional PA programs, access to resources on PA, and accessibility to community PA programs. Findings highlight the need to address factors influencing PA behaviour for individuals with PD.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.129
GPT teacher head0.373
Teacher spread0.244 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Adapted Physical ActivitySame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207