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Record W7009576195

El estado de ánimo precompetitivo en un equipo de fútbol profesional : un estudio entre jugadores titulares y suplentes

2011· article· es· W7009576195 on OpenAlexaboutno aff

Bibliographic record

VenueDigitum: Institutional Repository of the University of Murcia (University of Murcia) · 2011
Typearticle
Languagees
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPoison controlQuarter (Canadian coin)Casual
DOInot available

Abstract

fetched live from OpenAlex

El objetivo de este artículo se centra en profundizar en la importancia que tiene el estado anímico de los jugadores de un equipo de fútbol profesional, abordando su estudio desde un enfoque novedoso como es la comparación de jugadores titulares y suplentes.. La muestra la componen los jugadores de la AD Alcorcón de la Segunda División B del fútbol español (Madrid, España), que fueron evaluados mediante la versión reducida del Profile of Mood State (POMS, MacNair, Lorr and Droppelmann, 1971), en la versión de 29 ítems (Fuentes, García-Merita, Meliá and Balaguer, 1995), durante cinco partidos de la temporada regular -inmediatamente anteriores a la disputa de la fase de ascenso a 2ª División A-.Mediante la prueba no paramétrica para muestras independientes U de Mann-Whitney, se pone de manifiesto que existen diferencias significativas en dos de las cinco escalas del POMS, en concreto, en la escala de cólera y depresión.Los resultados permiten reflexionar sobre las estrategias de optimización del rendimiento psicológico de los jugadores titulares y suplentes en momentos clave de la temporada, ayudando a los entrenadores a crear las condiciones para mejorar las puntuaciones ofrecidas por jugadores que se alejan del perfil ideal "iceberg" (Morgan, 1980a(Morgan, , 1980 b) b).

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)0.005
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 score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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
Published2011
Admission routes1
Has abstractyes

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