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Record W4293117546 · doi:10.1007/s40279-022-01752-6

Top 10 International Priorities for Physical Fitness Research and Surveillance Among Children and Adolescents: A Twin-Panel Delphi Study

2022· article· en· W4293117546 on OpenAlexafffund
Kai Zhang, César Agostinis‐Sobrinho, Lars Bo Andersen, Laura Basterfield, Daniel Berglind, Dylan Blain, Cristina Cadenas‐Sánchez, Christine Cameron, Valerie Carson, Rachel C. Colley, Tamás Csányi, Avery D. Faigenbaum, Antônio García‐Hermoso, Thayse Natacha Gomes, Aidan Gribbon, Ian Janssen, Gregor Jurak, Mónika Kaj, Tetsuhiro Kidokoro, Kirstin N. Lane, Yang Liu, Marie Löf, David R. Lubans, Costan G. Magnussen, Taru Manyanga, Ryan McGrath, Jorge Mota, Tim Olds, Vincent Onywera, Francisco B. Ortega, Adewale L. Oyeyemi, Stéphanie A. Prince, Robinson Ramírez‐Vélez, Karen Roberts, Lukáš Rubín, Jennifer Servais, Diego Augusto Santos Silva, Danilo R. Silva, Jordan Smith, Yi Song, Gareth Stratton, Brian W. Timmons, Grant R. Tomkinson, Mark S. Tremblay, Stephen Heung‐Sang Wong, Brooklyn J. Fraser

Bibliographic record

VenueSports Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsCarleton UniversityMcMaster UniversityUniversity of Northern British ColumbiaQueen's UniversityUniversity of AlbertaUniversity of VictoriaStatistics CanadaChildren's Hospital of Eastern OntarioCanadian Fitness and Lifestyle Research InstitutePublic Health Agency of CanadaUniversity of Ottawa
FundersPublic Health Agency of Canada
KeywordsDelphi methodLikert scalePhysical fitnessSports medicinePanel dataMedicineDelphiPsychologyApplied psychologyMedical educationPhysical therapyStatisticsComputer scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The measurement of physical fitness has a history that dates back nearly 200 years. Recently, there has been an increase in international research and surveillance on physical fitness creating a need for setting international priorities that could help guide future efforts. OBJECTIVE: This study aimed to produce a list of the top 10 international priorities for research and surveillance on physical fitness among children and adolescents. METHODS: Using a twin-panel Delphi method, two independent panels consisting of 46 international experts were identified (panel 1 = 28, panel 2 = 18). The panel participants were asked to list up to five priorities for research or surveillance (round 1), and then rated the items from their own panel on a 5-point Likert scale of importance (round 2). In round 3, experts were asked to rate the priorities identified by the other panel. RESULTS: = 0.77, p < 0.01) in the priorities identified. The list of the final top 10 priorities included (i) "conduct longitudinal studies to assess changes in fitness and associations with health". This was followed by (ii) "use fitness surveillance to inform decision making", and (iii) "implement regular and consistent international/national fitness surveys using common measures". CONCLUSIONS: The priorities identified in this study provide guidance for future international collaborations and research efforts on the physical fitness of children and adolescents over the next decade and beyond.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.036
GPT teacher head0.346
Teacher spread0.310 · 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

Citations100
Published2022
Admission routes2
Has abstractyes

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