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Record W4232201381 · doi:10.15760/etd.5434

Determinants of elite athletes' commitment to sport : examination of the sport commitment model in the professional sport domain

2000· report· en· W4232201381 on OpenAlexaboutno aff
Tammy Hall

Bibliographic record

Venuenot available
Typereport
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyFootballAthletesSocial psychologyConfirmatory factor analysisLeagueVariance (accounting)Construct validityTeam sportScale (ratio)Construct (python library)Multivariate analysis of varianceTest (biology)Structural equation modelingEliteApplied psychologyClinical psychologyPsychometricsStatisticsPolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

This study examined the applicability of the Sport Commitment Model for a group of elite, professional athletes. The model proposes that an athlete's commitment will increase as sport enjoyment, personal investments, social constraints, and involvement opportunities increase and will decrease with an increase in involvement opportunities. The influence of identification as an athlete, a determinant of commitment not included in the original model, was also examined. One hundred and eighty three professional football players from the Canadian Football League (CFL) (n = 121) and National Football League (NFL) (n = 69) participated in the study. Each subject completed a modified version of the original questionnaire developed to test the constructs in the Sport Commitment Model (Scanlan, Simons, Carpenter, Schmidt, & Keeler, 1993) during a team meeting. Internal consistency reliabilities for the final items in all seven scales were acceptable. Confirmatory factor analysis indicated marginal overall fit (AGFI = 0.757) demonstrating good construct validity and discriminant validity for each scale. Zero-order correlations between commitment and its predictor constructs were significant and in the hypothesized direction for all predictor constructs except social constraints. The correlation between commitment and social constraints was negative and nonsignificant. The simultaneous regression analysis results found the predictor constructs accounted for 38% of the variance in commitment. Identification uniquely accounted for the most variance followed by enjoyment, involvement alternatives, and involvement opportunities. Only personal investments and social constraints did not contribute a significant amount of unique variance to sport commitment. The importance and meaning of the relationships between commitment and its determinants for professional athletes are discussed, as well as directions for future research.

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)0.003
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.348
Teacher spread0.302 · 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
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

Citations1
Published2000
Admission routes1
Has abstractyes

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