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Record W1965255407 · doi:10.1080/2159676x.2012.686060

Coaches of athletes with a physical disability: a look at their learning experiences

2012· article· en· W1965255407 on OpenAlexafffund
Sarah McMaster, Diane M. Culver, Penny Werthner

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2012
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
FundersMcMaster UniversityUniversity of Ottawa
KeywordsThematic analysisPsychologyAthletesFormal learningInformal learningApplied psychologyQualitative researchMedical educationPedagogyMedicineSociologyPhysical therapy

Abstract

fetched live from OpenAlex

Literature has shown that research on coaches of athletes with a physical disability is lacking. The purpose of this study is to examine the learning experiences of coaches in disability sport. Five coaches participated in this study. Data included two semi-structured interviews and two non-participant observation sessions with each coach. Thematic analysis was employed using theory of human learning as a theoretical framework. Results indicated three main themes influencing the coaches’ learning and development: (a) their biographies, (b) how they chose to learn (i.e. through formal, non-formal and informal learning situations) and (c) the learning opportunities provided by their sport. The coaches noted the usefulness of varied learning situations, and identified a lack of resources and few non-formal and formal learning opportunities specific to disability sport.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0080.004
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.285
GPT teacher head0.559
Teacher spread0.274 · 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 designQualitative
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

Citations100
Published2012
Admission routes2
Has abstractyes

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