Social support and sports participation motivations of female adolescents in India - study of age transition and achievement level
Bibliographic record
Abstract
Abstract Motivation is likely a pivotal factor contributing to sports participation. In India, because of the unsupportive sports/physical education environment, girls participate less in sports and other physical activities than boys. Therefore, embedding a planned healthy environment with good social support may maximize and, consequently, increase their participation in various sports and physical activities. Considering this unabated dialogue and assuming that motivation is a pivotal need for female sports participation, this study examined the differences in motivation to participate in sports among female adolescents across three age categories: early (11–14 years old), middle (15–18 years old), and late (19–21 years old) adolescence. A total of 528 female adolescents from Assam, India participated in the study. Participation in physical education courses was the criterion for the participants, and they were invited to complete the Participation Motivation Questionnaire. A factor analysis with direct oblimin rotation using the maximum likelihood extraction method yielded a 23-item, five-factor structure that exhibited moderate internal consistency. An ANOVA revealed significant differences across all three age categories regarding all the extracted factors. Moreover, t-tests of all sub-factors identified significant differences between the two achievement levels of district-level and inter-district-level athletes. This study provides sound psychometric and comparative results that are useful for education and formative reviews in applied settings and research purposes. It also serves as a measurement tool for embedding motivation to participate in physical activities and sports among female adolescents and the development of strategic ideas to assist female adolescents from less privileged areas to experience healthy growth and a healthy lifestyle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".