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Enregistrement W4205568304 · doi:10.5539/ies.v15n1p111

The Effect of Altruistic Behaviors of Sports Sciences Faculty Students on the Decision of Forgiveness: A Structural Equality Model Investigation

2022· article· en· W4205568304 sur OpenAlexvenueno aff
Hacer Ozge Baydar Arican

Notice bibliographique

RevueInternational Education Studies · 2022
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueIslamic Finance and Banking Studies
Établissements canadiensnon disponible
Organismes subventionnairesGazi Üniversitesi
Mots-clésPsychologyAthletesAltruism (biology)ForgivenessStructural equation modelingScale (ratio)Physical educationSocial psychologyDemographyPhysical therapyMathematics educationMedicineMathematicsSociologyStatistics

Résumé

récupéré en direct d'OpenAlex

The purpose of the present study was to examine the effects of altruism behaviors on the forgiveness decision of athletes and sedentary students who continued education in the faculty of sports sciences with the Structural Equation Model. To this end, the Study Group consisted of a total of 200 athletes and sedentary students, 108 female and 92 male, who were selected with the Convenient Sampling Method, who continued education at Gazi University Faculty of Sport Sciences. When the distribution was examined according to gender, 58.5% of the sedentary group was female and 41.5% was male. The rate of women in the athlete group was 47.6%, and the rate of men was 52.4%. When the distribution was examined according to age groups, the rate of people in the 17-20 group in the sedentary group was 31.4%, the rate of people in the 21-24 age group was 55.9%, and the rate of people who were older than 25 was 12.7%. The rate of individuals who were aged 17-20 is 48.8% in the athlete group, the rate of individuals aged 21-24 was 34.1%, and the rate of individuals who were older than 25 years was 17.1%. The “Forgiveness Decision Scale” and the “Altruism Scale” were used as measurement tools in addition to the personal information form that was created by the researcher to obtain data in the study. The Structural Equation Model and the t-test for independent groups, One-Way Analysis of Variance (ANOVA), percentage, frequency, and descriptive statistical analyzes were used in the analysis of the data. When the study findings were examined, the altruism scale sub-dimensions in the sedentary and athlete groups did not differ at significant levels according to the gender variable (p>0.05), and the forgiveness decision scale differed at significant levels in both the athlete and sedentary groups according to gender. The level of forgiveness decision of women (3.45±0.57) was higher than that of men (3.19±0.55) in the sedentary group. Similarly, the level of forgiveness decision of women was higher (3.57±0.64) in the athlete group than that of men (3.41±0.61). When the changes of forgiveness decision scale according to age groups were examined, forgiveness in sedentary people did not differ at significant levels according to age groups (p<0.05), and it did not create a significant difference according to age groups in athletes (p<0.05). The Structural Equation Model was established and tested for both groups separately to determine the effect of altruism on forgiveness in sedentary and athletes. When the goodness of fit coefficients that were calculated by the Structural Equation Model was examined, both models showed a good fit. According to the Correlation Analysis that was made to determine the relations between the altruism scale and forgiveness, the scale of forgiveness was negative at 31.4% in athletes in financial aid, positive at 69.9% with help in traumatic situations, and 55.1% in help in the educational process (p<0.05). No significant relations were detected between forgiveness and the sub-dimensions of the altruism scale in sedentary people (p>0.05).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,014
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,074

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,026
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,004
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0020,001
Science ouverte0,0020,003
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,059
Tête enseignante GPT0,392
Écart entre enseignants0,333 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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