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Record W2979535155 · doi:10.1186/s12890-019-0985-5

Using photo-elicitation to explore perceptions of physical activity among young people with cystic fibrosis

2019· article· en· W2979535155 on OpenAlexfundno aff
Sarah Denford, Denise M. Hill, Kelly A. Mackintosh, Melitta A. McNarry, Alan R. Barker, Craig A. Williams

Bibliographic record

VenueBMC Pulmonary Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsnot available
FundersHospital for Sick ChildrenGreat Ormond Street Hospital for ChildrenSwansea UniversityLa Trobe UniversityUniversity College LondonUniversity of ExeterCystic Fibrosis Trust
KeywordsPhysical activityMedicinePerceptionAutonomyPsychological interventionPhoto elicitationPopulationCystic fibrosisPsychologyNursingEnvironmental healthPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Physical activity is recommended in the management of cystic fibrosis (CF). The aim of this study was to explore motives, barriers and enablers to physical activity among this population. METHODS: Twelve participants (12-18 years) were recruited via convenience sampling. Photo-elicitation alongside semi-structured interviews were used to explore participants' views and experiences of physical activity. RESULTS: Our findings revealed motives for physical activity including health, enjoyment and autonomy. Those with families who valued physical activity tended to have positive attitudes towards physical activity, and valued and integrated it into their lives. Moreover, they were likely to be intrinsically motivated to be active. Several factors enable and act as barriers to physical activity. Whilst CF influenced physical activity, the majority of enablers and barriers raised where congruent with the general populations. CONCLUSION: This study provides support that healthcare providers should encourage both young people with CF and their families to be active, and subsequently informs the development of clinical interventions to support physical activity among young people with CF and their families.

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)0.001
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 score0.607
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.347
Teacher spread0.303 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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