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Record W3048498291 · doi:10.3390/ijerph17165832

Stopping the Drop: Examining the Impact of a Pilot Physical Literacy-Based Intervention Program on Physical Activity Behaviours and Fitness during the Transition into University

2020· article· en· W3048498291 on OpenAlexaff
Matthew Kwan, Jeffrey D. Graham, C. W. R. Healey, Natalie Paolucci, Denver M. Y. Brown

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2020
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsMcMaster UniversityHealth Sciences Centre
Fundersnot available
KeywordsCardiorespiratory fitnessIntervention (counseling)Physical activityRepeated measures designPsychologyGerontologyPhysical fitnessPhysical therapyDrop outAnalysis of varianceLiteracyMedicinePedagogyMathematicsInternal medicine

Abstract

fetched live from OpenAlex

The move to university is a major life transition associated with precipitous declines in physical activity (PA). While it remains unclear how these declines can be best attenuated, the concept of physical literacy (PL) or enhancements of PL may be a promising modality to promote PA during life transitions. The purpose of the current study was to evaluate the impact of a pilot PL-based intervention on PA and fitness for students transitioning into university. Participants included 65 first-year students (Mage = 17.85 ± 0.51; n = 46 females), enrolled in a quasi-experimental study. Intervention participants (n = 26) participated in a 12-week novel movement skills program conducted in a group-based environment. Results from the 2 × 2 repeated measures ANOVA found moderate effects in the time by condition interaction F(1,56) = 2.70, p = 0.11, ηp2 = 0.08 for PA behaviors as well as for estimated cardiorespiratory fitness F(1,61) = 8.35, p = 0.005, ηp2 = 0.12. Findings from our pilot program suggest that PL may be an effective modality to help first-year university students maintain fitness and attenuate the declines in PA behaviors when transitioning into university. Similar trials with larger samples are required.

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)0.001
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 score0.979
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.076
GPT teacher head0.408
Teacher spread0.332 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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