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Record W2181164973 · doi:10.1260/1747-9541.10.4.699

The Intervention Tone of Coaches' Behaviour: Development of the Assessment of Coaching Tone (ACT) Observational Coding System

2015· article· en· W2181164973 on OpenAlexaff
Karl Erickson, Jean Côté

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2015
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsQueen's University
Fundersnot available
KeywordsCoachingCoding (social sciences)Observational studyPsychologyTone (literature)Applied psychologyInter-rater reliabilityProtocol (science)Intervention (counseling)Developmental psychologyMedicinePsychotherapistAlternative medicine

Abstract

fetched live from OpenAlex

The importance of coaches' interactive behaviour with respect to athlete development has long been recognized. While a number of observational coding systems exist to record the instructional content of coaches' interactive behaviour, none is designed to explicitly capture the intervention tone of these interactions – ‘how’ coaches say what they say. The current project entailed the development of a new behavioural coding system designed to focus on the intervention tone of youth sport coaches' interactive behaviour. Behaviour categories were developed through an iterative combination of literature review and observation of recorded youth sport coaching sessions. A coder training protocol was developed and refined until coders consistently met a minimum standard of agreement with respect to both inter- and intra-rater reliability. The full coding system was then initially validated across six different team and individual youth sports in multiple contexts over a one year period.

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)0.046
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.046
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.082
GPT teacher head0.401
Teacher spread0.319 · 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 designObservational
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

Citations31
Published2015
Admission routes1
Has abstractyes

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