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Record W4212964812 · doi:10.3389/fpsyg.2021.698673

The Effects of Verbal Encouragement and Compliments on Physical Performance and Psychophysiological Responses During the Repeated Change of Direction Sprint Test

2022· article· en· W4212964812 on OpenAlexaff
Hajer Sahli, Monoem Haddad, Nidhal Jebabli, Faten Sahli, Ibrahim Ouergui, Nejmeddine Ouerghi, Nicola Luigi Bragazzi, Makrem Zghibi

Bibliographic record

VenueFrontiers in Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsYork University
Fundersnot available
KeywordsSprintPsychologyFeelingTest (biology)Rating of perceived exertionPerceived exertionRepeated measures designDevelopmental psychologySocial psychologyPhysical therapyHeart rateStatistics

Abstract

fetched live from OpenAlex

The general and sports psychology research is limited regarding the difference between the effects of verbal encouragement (VE) or compliment methods during high-intensity functional exercise testing. The purpose of this study was to explore the effects of VE and compliments on the performance of the repeated change-of-direction (RCOD) sprint test. A total of 36 male students in secondary school participated voluntarily in the study. They were divided equally into three homogeneous groups [VE group, compliment group (CG), and control group) and performed a standardized one repetition RCOD. The RCOD (6 × 20 m with 25-s active recovery) test consisted of a 100°change in the direction at every 4 m. Outcomes included performance indices (fast time, average time, and total time), rating of perceived exertion (RPE), and feeling scale scores. VE and the compliment increased the performance indices and RPE compared with the control group. In conclusion, VE during the exercise testing would be more beneficial for optimal performance and RPE compared with the compliment and control groups. However, the moods, during RCOD, reproduce more positively during compliment conditions than the VE and control groups.

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 score0.374
Threshold uncertainty score0.271

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 head0.033
GPT teacher head0.361
Teacher spread0.328 · 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

Citations29
Published2022
Admission routes1
Has abstractyes

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