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Record W3209442363

Exploring first-year university students' barriers and facilitators to meeting the recommendations in the Canadian 24-Hour Movement Guidelines for Adults

2020· article· en· W3209442363 on OpenAlexaboutno aff
Nicole Giouridis, Stephanie M. Flood, Julia McKenna, Madelaine Gierc, Guy Faulkner, Amy E. Latimer‐Cheung, Jennifer R. Tomasone

Bibliographic record

VenueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository) · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisFocus groupMedical educationPsychologyImplementation researchIntervention (counseling)Qualitative researchMedicinePsychological interventionNursingSociology
DOInot available

Abstract

fetched live from OpenAlex

First-year undergraduate students report low adherence to all movement behaviour (physical activity, sedentary behaviour and sleep) recommendations in the Canadian 24-Hour Movement Guidelines for Adults aged 18-64 years (24HMG). A first step towards the development of an implementation intervention is to understand the barriers and facilitators faced when attempting the target behaviours. The purpose of this study was to determine the multilevel factors that facilitate or prevent first-year undergraduate students' movement behaviours. Thirteen focus groups were conducted with first-year students from Queen's University (n=8) and the University of British Columbia (n=6). Inductive thematic analysis identified three themes and 13 sub-themes, which included multiple barriers and facilitators students face when trying to meet the 24HMG recommendations. Subsequently, themes and sub-themes were deductively categorized onto both the Consolidated Framework for Implementation Research (CFIR) and the Theoretical Domains Framework (TDF) to understand barriers and facilitators to implementation and behaviour change, respectively. CFIR coding suggested that the challenges students face directly relate to the individual and the value placed on meeting 24HMG recommendations, as well as indirectly to the social and physical environments. TDF coding highlighted the complexity of enacting the 24HMG, given each of the behaviours are not 'single-incidence' behaviours; they occur in various contexts, at different times and frequencies, and require the targeting of multiple constructs to foster multiple-behaviour change. Findings provide a foundation that will contribute to the development of an evidence- and theory-based implementation intervention to improve 24HMG adherence among first-year university students.Acknowledgments: This study was funded by the Public Health Agency of Canada (grant number 1920-HQ-000004) and supported by the Canadian Society for Exercise Physiology.

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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.651
Threshold uncertainty score0.693

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.266
GPT teacher head0.449
Teacher spread0.183 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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