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Record W4398221679 · doi:10.1080/24704067.2024.2353620

Examining the Prevalence of Burnout among Golf Operations Employees in Canada

2024· article· en· W4398221679 on OpenAlexaffabout
Sheldon Fetter, Jess C. Dixon

Bibliographic record

VenueJournal of Global Sport Management · 2024
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBurnoutPsychologyApplied psychologyClinical psychology

Abstract

fetched live from OpenAlex

The burnout phenomenon has been explored in many industries. However, there has been minimal research among those who work in various sport occupations. This project examined the prevalence of burnout among golf operations employees across Canada and the effects of various predictors of the burnout phenomenon. The conservation of resource theory (COR) was utilized, as it has been widely employed in the study of burnout among employees. The sample included 112 golf operations employees, with a mean age of 38.12 years. A hierarchical linear regression analysis revealed positive significant relationships between burnout and the average number of hours worked, post-secondary education, and turnover intentions. Further, a negative significant relationship was found among burnout and age. The results of this study stress the importance of employers taking the time to understand the needs of their employees in this unique environment to ensure they have the proper resources to mitigate negative behavioural and organizational predictors of burnout.

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 score0.156
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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 routes2
Has abstractyes

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