Comparative analysis of coaches' perception of their capability, opportunity, and motivation to train athletes with special educational needs

Background: In ensuring equal opportunities for all athletes, including those with special educational needs (SEN), coaches play a key role, as their inclusive competence determines the creation of an inclusive sports environment and affects the quality of the training process. Previous studies using the COM-B model have shown that Ukrainian coaches rate their readiness to train athletes with SEN low. Since this depends on various factors, it is important to understand how different socio-demographic characteristics may influence their responses and behavior. Study Aim: to conduct a comparative analysis of Ukrainian coaches’ perceptions of their capability, opportunities, and motivation to train athletes with special educational needs, which shape their behavior, based on socio-demographic differences and their own coaching experience. Material and Methods. The survey, conducted between September 2023 and March 2024, involved 379 Ukrainian coaches from 58 sports disciplines, with an average age of 37 (±14) years. Data collection was carried out via Google Forms, and statistical calculations were performed using Microsoft Office Excel 2010 and IBM SPSS Statistics Version 27. Methods included questionnaires and a range of general scientific and mathematical-statistical methods (descriptive statistics, Mann-Whitney U test, and Kruskal-Wallis H test). Results. Statistically significant differences were found in coaches' perceptions of their capability, opportunity, and motivation to train athletes with SEN based on the following factors: sex (2,09≤|Z|≤3,41; 0,01<p≤0,04; small effect size); experience with SEN athletes (5,90≤|Z|≤8,06; p<0,01; moderate effect size); self-identification as an inclusive coach (5,67≤|Z|≤8,84; p<0,01; рmoderate to large effect size). No statistically significant differences were found based on age, coaching experience, education level, or specialties. Conclusions. The absence of differences in responses between coaches with and without coaching-specific education indicates that, at present, professional training does not adequately prepare coaches to work with athletes with SEN. These results should be considered when developing training programs and professional development initiatives related to inclusion in sports.

Authors

Institutions

Publication Details

Journal
Слобожанський науково-спортивний вісник
Published
2025-06-16
DOI
https://doi.org/10.15391/snsv.2025-2.04
Primary Topic
Motivation and Self-Concept in Sports
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparative analysis of coaches' perception of their capability, opportunity, and motivation to train athletes with special educational needs

Олеся Шевчук, Viktoriia Marynych, Iryna Коhut
Слобожанський науково-спортивний вісник
Motivation and Self-Concept in Sports
article

Comparative analysis of coaches' perception of their capability, opportunity, and motivation to train athletes with special educational needs

Олеся Шевчук, Viktoriia Marynych, Iryna Коhut
article en

Abstract

Background: In ensuring equal opportunities for all athletes, including those with special educational needs (SEN), coaches play a key role, as their inclusive competence determines the creation of an inclusive sports environment and affects the quality of the training process. Previous studies using the COM-B model have shown that Ukrainian coaches rate their readiness to train athletes with SEN low. Since this depends on various factors, it is important to understand how different socio-demographic characteristics may influence their responses and behavior. Study Aim: to conduct a comparative analysis of Ukrainian coaches’ perceptions of their capability, opportunities, and motivation to train athletes with special educational needs, which shape their behavior, based on socio-demographic differences and their own coaching experience. Material and Methods. The survey, conducted between September 2023 and March 2024, involved 379 Ukrainian coaches from 58 sports disciplines, with an average age of 37 (±14) years. Data collection was carried out via Google Forms, and statistical calculations were performed using Microsoft Office Excel 2010 and IBM SPSS Statistics Version 27. Methods included questionnaires and a range of general scientific and mathematical-statistical methods (descriptive statistics, Mann-Whitney U test, and Kruskal-Wallis H test). Results. Statistically significant differences were found in coaches' perceptions of their capability, opportunity, and motivation to train athletes with SEN based on the following factors: sex (2,09≤|Z|≤3,41; 0,01<p≤0,04; small effect size); experience with SEN athletes (5,90≤|Z|≤8,06; p<0,01; moderate effect size); self-identification as an inclusive coach (5,67≤|Z|≤8,84; p<0,01; рmoderate to large effect size). No statistically significant differences were found based on age, coaching experience, education level, or specialties. Conclusions. The absence of differences in responses between coaches with and without coaching-specific education indicates that, at present, professional training does not adequately prepare coaches to work with athletes with SEN. These results should be considered when developing training programs and professional development initiatives related to inclusion in sports.

Слобожанський науково-спортивний вісникVol. 29(2)
National University of Ukraine on Physical Education and Sport (UA)
Openalex Percentile: Top 85%
Motivation and Self-Concept in Sports
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.