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Record W4402535255 · doi:10.1016/j.paid.2024.112868

Alexithymia in athletic populations: Prevalence, and relationship with self-control and reinvestment

2024· article· en· W4402535255 on OpenAlexaboutno aff
Hannah L. Graham, Ruth Boat, Simon B. Cooper, Noel P. Kinrade

Bibliographic record

VenuePersonality and Individual Differences · 2024
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyClinical psychologyControl (management)Developmental psychologyManagement

Abstract

fetched live from OpenAlex

Alexithymia is the inability to identify or describe feelings, with a tendency for externally oriented thinking; these facets have potential benefits for athletic performance. This study explored the prevalence of alexithymia among athletes, across different sports and athletic ability, and considered the relationship between alexithymia and trait self-control, and between alexithymia and reinvestment. Athletes ( N = 787) completed a 15-min online survey which comprised self-report questionnaires (e.g., demographic, Toronto Alexithymia Scale, Movement Specific Reinvestment Scale (MSRS), Decision Specific Reinvestment Scale (DSRS), and The Brief Self-Control Scale). The overall prevalence of high-alexithymia was notable in an athletic population; analyzes revealed that high-static-dynamic sports had higher alexithymia scores compared to low-static-dynamic sports. Athletes with higher alexithymia scores were related to lower trait self-control, in addition to higher MSRS and DSRS scores. The findings of the present study suggest that alexithymic athletes experience emotional dysregulation issues, are more likely to engage in risky behaviors, and engage in processes which are detrimental to their performance. This study represents an initial exploration, and future research should expand upon these findings to fully determine the performance outcomes of alexithymia in sport.

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)0.000
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 score0.015
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.052
GPT teacher head0.289
Teacher spread0.237 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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