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Record W2153060116 · doi:10.1123/jpah.2014-0168

Results from Finland’s 2014 Report Card on Physical Activity for Children and Youth

2014· article· en· W2153060116 on OpenAlexaboutno aff
Jarmo Liukkonen, Timo Jaakkola, Sami Kokko, Arto Gråstén, Sami Yli‐Piipari, Pasi Koski, Jorma Tynjälä, Anne Soini, Timo Ståhl, Tuija Tammelin

Bibliographic record

VenueJournal of Physical Activity and Health · 2014
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersJyväskylän YliopistoHelsingin Yliopisto
KeywordsReport cardPhysical activityGrading (engineering)Positive Youth DevelopmentPsychologyGovernment (linguistics)Sedentary behaviorMedical educationLife styleApplied psychologyMedicineDevelopmental psychologyPhysical therapyPedagogyEngineering

Abstract

fetched live from OpenAlex

The Finnish 2014 Report Card on Physical Activity (PA) for Children and Youth is the first assessment of Finland's efforts in promoting and facilitating PA opportunities for children and youth using the Active Healthy Kids Canada grading system. The Report Card relies primarily on research findings from 6 Research Institutes, coordinated by the University of Jyväskylä. The Research Work Group convened to evaluate the aggregated evidence and assign grades for each of the 9 PA indicators, following the Canadian Report Card protocol. Grades from A (highest) to F (lowest) varied in Finland as follows: 1) Overall physical activity-fulfillment of recommendations (D), 2) Organized sport participation (C), 3) Active play (D), 4) Active transportation (B), 5) Sedentary behaviors (D), 6) Family and peers (C), 7) School (B), 8) Community and the built environment (B), and 9) Government (B). This comprehensive summary and assessment of indicators related to PA in Finnish children and youth indicates that Finland still has many challenges to promote a physically active life style for youth.

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)0.006
metaresearch head score (Gemma)0.022
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: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.198
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.009
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.004

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 head0.060
GPT teacher head0.366
Teacher spread0.306 · 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

Citations27
Published2014
Admission routes1
Has abstractyes

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