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Record W2899313796 · doi:10.1080/1612197x.2018.1536158

Examining the influence of athletes’ punishment experiences on decisions to cease participation in competitive hockey

2018· article· en· W2899313796 on OpenAlexaff
Anthony Battaglia, Gretchen Kerr, Ashley Stirling

Bibliographic record

VenueInternational Journal of Sport and Exercise Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAthletesPsychologyPunishment (psychology)Thematic analysisField hockeyIce hockeyCompetitive athletesSocial psychologySport psychologyTeam sportApplied psychologyQualitative researchFootballPhysical therapyPhysical medicine and rehabilitationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This study examined athletes’ experiences of punishment in competitive hockey and the potential influence of these experiences on their decisions to cease participation in the sport. Semi-structured interviews were conducted with 10 former female and male competitive hockey athletes, 18–19 years of age. Data were analysed using inductive thematic analysis. Participant accounts revealed that yelling, benching, and physical conditioning exercises were forms of punishment experienced regularly throughout their hockey involvement. These punishment experiences reportedly had negative effects on the participants’ perceived athletic worth, enjoyment and fun in hockey, and sport relationships, and enhanced their interests in other activities. Findings are interpreted to suggest that punishment experiences contributed to athletes’ decisions to cease participation in competitive hockey. Recommendations for future research and practice are suggested.

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 score0.067
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.045
GPT teacher head0.397
Teacher spread0.351 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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