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Record W2105609179 · doi:10.1123/tsp.20.3.314

Stressors, Coping, and Coping Effectiveness among Professional Rugby Union Players

2006· article· en· W2105609179 on OpenAlexaff
Adam R. Nicholls, Nicholas L. Holt, Remco Polman, Jonny Bloomfield

Bibliographic record

VenueThe Sport Psychologist · 2006
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStressorCoping (psychology)PsychologyChecklistClinical psychologyLikert scaleApplied psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The overall purpose of this study was to examine stressors, coping strategies, and perceived coping effectiveness among professional rugby union players. Eight first class professional male rugby union players maintained diaries over a 28-day period. The diaries included a stressor checklist, an open-ended coping response section, and a Likert-type scale evaluation of coping effectiveness. Total reported stressors and coping strategies were tallied and analyzed longitudinally. The most frequently cited stressors were injury concerns, mental errors, and physical errors. The most frequently cited coping strategies were increased concentration, blocking, positive reappraisal, and being focused on the task. The most effective coping strategies were focusing on task and increasing effort. Professional rugby players use a variety of different coping strategies in order to manage the stressors they experience, but the effectiveness of their coping attempts can vary.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.313
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

Citations154
Published2006
Admission routes1
Has abstractyes

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