MétaCan
Menu
Back to cohort
Record W2549386343 · doi:10.1123/jpah.2016-0309

Results From Wales’ 2016 Report Card on Physical Activity for Children and Youth: Is Wales Turning the Tide on Children’s Inactivity?

2016· article· en· W2549386343 on OpenAlexaboutno aff
Richard Tyler, Marianne Mannello, Rebecca Mattingley, Chris Roberts, Robert Sage, Suzan R. Taylor, Malcolm Ward, Simon Williams, Gareth Stratton

Bibliographic record

VenueJournal of Physical Activity and Health · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersSwansea UniversityLlywodraeth Cymru
KeywordsReport cardPhysical activityPsychologyMedicinePhysical therapyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: This is the second Active Healthy Kids Wales Report Card. The 2016 version consolidates and translates research related to physical activity (PA) among children and youth in Wales, and aims to raise the awareness of children's engagement in PA and sedentary behaviors. METHODS: Ten PA indicators were graded using the Active Healthy Kids-Canada Report Card methodology involving a synthesis and expert consensus of the best available evidence. RESULTS: Grades were assigned as follows: Overall PA, D+; Organized Sport Participation, C; Active and Outdoor Play, C; Active Transportation, C; Sedentary Behaviors, D-; Physical Literacy, INC; Family and Peer Influences, D+; School, B; Community and the Built Environment, C; and National Government Policy, Strategies, and Investments, B-. CONCLUSIONS: Despite the existence of sound policies, programs, and infrastructure, PA levels of children and youth in Wales are one of the lowest and sedentary behavior one of the highest globally. From the 2014 Report Card, the Family and Peer Influences grade improved from D to D+, whereas Community and the Built Environment dropped from B to C. These results indicate that a concerted effort is required to increase PA and decrease sedentary time in children and young people in Wales.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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 head0.038
GPT teacher head0.330
Teacher spread0.293 · 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

Citations30
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physical Activity and HealthSame topicObesity, Physical Activity, DietFrench-language works237,207