Female paralympic athlete preferences of effective coaching practices
Notice bibliographique
Résumé
According to the World Health Organization (World Health Organization, 2016), over one billion people, or 15% of the population, have some type of disability. In Canada, approximately 3.8 million people reported having a disability, and over three million adults (11%) reported having a physical disability (Statistics Canada, 2012a). People living with physical disabilities are at a higher risk of developing secondary physical and mental health conditions such as fatigue, obesity, and depression (Goodwin & Compton, 2004; Heron, Kee, Cupples, & Tully, 2015). The occurrence of secondary conditions has been associated with a lower quality of life and decreased independence for people with a physical disability (Motl & McAuley, 2014). Physical activity is one way to decrease the severity and prevalence of these secondary conditions (Goodwin & Compton, 2004). One type of physical activity is sport, which has been identified as a way of providing many psychological, physiological, and social benefits for people with a physical disability (Stephens, Neil, & Smith, 2012). The largest sporting competition for elite athletes with a physical disability is the Paralympic Games and despite the exponential growth of the Games, the same growth and development has not occurred with empirical literature in this context, for either the coaches or the athletes participating in this event. Additionally, most of the samples in elite disability sport have been male only or mixed gender, most attributable to the small proportion of female athletes competing as a Paralympian. Thus, the purpose of the current study was to explore female Paralympic athlete preferences of effective coaching practices. Individual semi-structured interviews were conducted with eight female Paralympic athletes who each attended multiple Paralympic Games and achieved an average of eight combined Paralympic and Para Pan American medals. Interviews were transcribed verbatim and organized into themes and subthemes using thematic analysis, which provided the reader with a comprehensive understanding of each participants' experience (Braun & Clarke, 2013; Sparkes & Smith, 2014). Results from the analysis revealed that athletes preferred coaches who were able to adapt to the various needs of the individual, were knowledgeable of their sport, engaged in effective communication, and challenged athletes to reach their potential. Athletes also described negative coaching experiences and described coaches who insulted them based on their gender and disability, and engaged in selfish or manipulative behaviours. The athletes felt the coaches' behaviours impacted their satisfaction and success on both a personal and professional level. These results add to the small body of coaching knowledge in disability sport, and is one of the first studies to include an all-female sample of Paralympic athletes.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».