The influence of a collaborative structure on the sport promotional effect of a youth sport camp: a multilevel-analysis
Notice bibliographique
Résumé
AIM. Already since the 1990s, collaborative structures have been frequently used to solve complex public problems, such as health related issues (Turrini, Cristofoli, Frosini, & Nasi, 2010). Physical inactivity is considered one of the most crucial health issues among children, therefore the promotion of physical activity and sports is an important task of governments. Field information shows that governments often rely on collaborative structures to meet this health tasks. However, literature provides few indications on whether collaborative structures are a preferable way of organizing sport promoting programs. In this research, we compare the effectiveness of youth sport camps as sport promotion programs, organized in a collaborative structure and sport camps organized by one institution. THEORETICAL BACKGROUND. Local governments increasingly rely on partners in order to acquire resources and serve their clients better (Thibault, Frisby, & Kikulis, 1999). Since through a collaborative structure effectiveness and coordination of service delivery is increased and fragmentation of services reduced (Turrini et al., 2010), we assume that collaboration with external partners may increase the potential positive effects of sport promotional interventions. An already long existing example of sport promotional interventions are youth sport camps. Almost 90% of Flemish municipalities organized sport camps in 2010 (Van Poppel, 2012). Van Poppel (2012) noted that 70% of the Flemish municipalities organized sport camps individually. If the municipality collaborates for the organization of sport camps with external partners, sport clubs (50%) and private organizations (40%) are the most common partners. Since sport camps have a sport promotional goal, we may assume that a sport camp was successful when the participants start to practice sport, or at least show more interest in sports, after participating in the camp. However, other factors influence sport participation of children. Thus, when analyzing the effect of collaboration in sport camps, we need to take into account previous research on correlations between sport participation of the child and parental support, parental beliefs and parental physical activity (Edwardson & Gorely, 2010) and the child’s type of motivation and degree of self-determination towards sports (Aelterman et al., 2012). METHODS. Data were collected using a questionnaire completed by 418 parents of participants in 50 youth sport camps of which 32 camps were organized in collaboration with an external partner and 18 camps had a single organizer. The parents completed an online questionnaire on current sport behavior of the child, parental support and beliefs, motivation towards sports, and the child’s change in perception of sports. We compare data on children’s sport behavior, motivation, camp experience, and sport club membership at level one in a multilevel analysis with the presence of collaboration as level two variable. RESULTS. Data collection of this study is still ongoing in April 2014. However, preliminary results on 150 respondents show that the majority of the participants (90%) are member of a sport club and most of them (70%) were already a member prior to the camp. Twenty percent of the participants, who were not members of sport club prior to the camp, but are in a club now, state that participating in the camp influenced their choice to subscribe in the sport club, and, this choice correlates with the fact that the camp is organized by a collaborative structure. Detailed multi-level analysis on the total sample, including the effect of the collaborative structure, will be conducted in the following weeks and the definitive results will be available for presentation at the 2014 EASM conference in September. CONCLUSION. This study wants to contribute to literature on sport promotion and the effect of a collaborative structure. For practitioners, this work can provide a useful support in the decision if they would or would not collaborate with external partners for the organization of sport promotional initiatives. REFERENCES Aelterman, N., Vansteenkiste, M., Van Keer, H., Van den Berghe, L., De Meyer, J., & Haerens, L. (2012). Students' Objectively Measured Physical Activity Levels and Engagement as a Function of Between-Class and Between-Student Differences in Motivation Toward Physical Education. Journal of Sport & Exercise Psychology, 34(4), 457-480. Edwardson, C. L., & Gorely, T. (2010). Parental influences on different types and intensities of physical activity in youth: A systematic review. Psychology of Sport and Exercise, 11(6), 522-535. doi: DOI 10.1016/j.psychsport.2010.05.001 Thibault, L., Frisby, W., & Kikulis, L. M. (1999). Interorganizational linkages in the delivery of local leisure services in Canada: Responding to economic, political and social pressures. Managing Leisure, 4(3), 125-141. Turrini, A., Cristofoli, D., Frosini, F., & Nasi, G. (2010). Networking literature about determinants of network effectiveness. Public Administration, 88(2), 528-550. Van Poppel, M. (2012). Cijferboek lokaal sportbeleid 2011-2013. Brussel: Departement Cultuur, Jeugd, Sport en Media.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,013 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,001 |
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 ».