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Record W4405888164 · doi:10.1177/08862605241303958

Acceptable or Not: An In-depth Analysis of Adolescent Competitive Athletes’ Perceptions on Abusive Coaching Behaviors

2024· article· en· W4405888164 on OpenAlexaboutno aff
Élise Marsollier, Denis Hauw, Fabienne Crettaz von Roten

Bibliographic record

VenueJournal of Interpersonal Violence · 2024
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingPsychologyAthletesPerceptionAbusive supervisionApplied psychologyPoison controlSocial psychologyDevelopmental psychologyClinical psychologyMedicinePhysical therapyPsychotherapistMedical emergency

Abstract

fetched live from OpenAlex

The present study aimed to conduct an in-depth analysis of adolescent competitive athletes' perceptions on abusive coaching behaviors. Our aims were thus to (a) identify the acceptable abusive coaching behaviors and (b) characterize qualitatively the criteria for the acceptance of abusive coaching behaviors. Based on the study goal, an Abusive Coaching Behavior Grid was developed and completed by 356 French-speaking athletes, among which 10 were interviewed to justify where they draw the line between acceptable and unacceptable coaching behaviors. Quantitative analysis showed that shaking, shouting at, or asking athletes to perform until exhaustion were considered acceptable. Quebec and female athletes tended to accept fewer different abusive behaviors, but there were no differences by sport characteristics. The perception on abusive coaching behaviors was influenced by expectations about the coaching role, negative effects of coaching behaviors, circumstances in which the behaviors occur, and the nature of behaviors. The present study raises the importance of questioning cognitive schemas shared by groups of athletes as well as the norms coaches convey and the behaviors they adopt.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.037
GPT teacher head0.385
Teacher spread0.348 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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