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Record W3210504895 · doi:10.1080/1612197x.2021.1992857

International society of sport psychology position stand: mental health through occupational health and safety in high performance sport

2021· article· en· W3210504895 on OpenAlexaff
Robert J. Schinke, Cole E. Giffin, Suzanne Cosh, Kitrina Douglas, Daniel Rhind, Chris Harwood, Gangyan Si, Athanasios Papaiounnou

Bibliographic record

VenueInternational Journal of Sport and Exercise Psychology · 2021
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsLaurentian University
Fundersnot available
KeywordsSport psychologyMental healthApplied psychologyPsychologyAthletesSection (typography)Occupational safety and healthIdeologySport managementPublic relationsPolitical sciencePoliticsMedicinePhysical therapyBusinessAdvertisingPsychiatry

Abstract

fetched live from OpenAlex

There is considerable attention devoted to athlete mental health in high-performance sport. The International Society of Sport Psychology seeks to extend this discourse by expanding the discussion through a lens of occupational health and safety (OHS). Drawing upon knowledge from OHS, the authors have considered existing mental health challenges as well as proactive and reactive strategies conducive of athlete safety. This Position Stand is structured into four sections. The authors introduce OHS and its relevance to high-performance sport in section one. Section two focuses on athlete mental health and the need for an OHS approach within sport organisations. Section three is focused particularly on athlete safety in relation to high-performance youth athletes. The fourth and final section is a broader ideological discussion of how OHS might vary in approach based on national sport systems and cultural orientation.

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 score0.062
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 head0.018
GPT teacher head0.377
Teacher spread0.360 · 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

Citations42
Published2021
Admission routes1
Has abstractyes

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