MétaCan
Menu
Back to cohort
Record W4392694430 · doi:10.1080/02640414.2024.2328971

Practices and perceptions of strength and conditioning training in female golf: A cross-sectional survey study of high-level amateur players

2024· article· en· W4392694430 on OpenAlexaff
Luke Robinson, Andrew Murray, Margo Mountjoy, Jack Wells, Dan Coughlan, Danny Glover, Rebecca Hembrough, Anthony N. Turner, Chris Bishop

Bibliographic record

VenueJournal of Sports Sciences · 2024
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAmateurPerceptionApplied psychologyPsychologyFlexibility (engineering)Training (meteorology)Physical therapyMedicineMathematicsGeographyStatistics

Abstract

fetched live from OpenAlex

This study aims to ascertain an in-depth understanding of current practices and perceptions of S&C training in high-level amateur female golfers. A cross-sectional, explorative survey study was constructed which asked questions relating to four key areas: i) general participant information, ii) current strength and conditioning (S&C) practices, iii) the perceived influence of S&C training on golf performance, and iv) knowledge and awareness of S&C. Results showed that the majority of female players had participated in some form of S&C training in the past, with the majority believing that clubhead speed and carry distance were the primary golfing metrics which could be positively impacted. More specifically, 91-97% of the players "Strongly agreed" or "Agreed" that the key physical characteristics for golf were strength and power for the lower and upper body, and flexibility. Interestingly, 58% of the players believed that S&C training should mimic the movement of the golf swing, which based off current evidence, is not how drive metrics and ultimately shots gained, can be maximised. This survey study provides useful information relating to the practices and perceptions of S&C training in high-level female amateur players and areas where education may be able to further advance player understanding of physical preparation.

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)0.002
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 score0.014
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
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 head0.053
GPT teacher head0.322
Teacher spread0.269 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sports SciencesSame topicSports Dynamics and BiomechanicsFrench-language works237,207