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Record W3083761757 · doi:10.1519/ssc.0000000000000592

Enriching Selves in Strength and Conditioning Society: A Multilevel Proposal to Enhance Strength and Conditioning Psychosocial Practice as Part of the Council on Accreditation of Strength and Conditioning Education

2020· article· en· W3083761757 on OpenAlexaff
Brian Gearity, Christoph Szedlak, Clayton Kuklick, Joseph P. Mills, Mary Kate Feit, Bettina Callary, Adam Feit, Maura Bergan

Bibliographic record

VenueStrength and conditioning journal · 2020
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsCape Breton University
Fundersnot available
KeywordsAccreditationPsychosocialCoachingConditioningPsychologyMedical educationEngineering ethicsMedicineEngineeringPsychotherapist

Abstract

fetched live from OpenAlex

ABSTRACT This review article advances the longstanding need to address psychosocial issues within strength and conditioning (S&C) that have renewed significance with the National Strength and Conditioning Association's entry into accreditation. First, a brief history is presented on the omission of psychosocial issues within S&C and the education of S&C practitioners. Second, the article provides an overview of learning theory and instructional strategies on how to develop essential psychosocial competencies of S&C practitioners. Third, multilevel recommendations are offered to address established calls to improve the education of S&C practitioners, ongoing accreditation efforts, and continued structural changes throughout the growing, global profession of S&C coaching.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.003
Open science0.0010.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.338
Teacher spread0.315 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations12
Published2020
Admission routes1
Has abstractyes

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