The influence of a collaborative structure on the sport promotional effect of a youth sport camp: a multilevel-analysis
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".