The Role of Sports in Promoting Social Inclusion and Health in Marginalized Communities
Bibliographic record
Abstract
Objective: This study aims to explore the role of sports in promoting social inclusion and health within marginalized communities. Methods and Materials: A qualitative research design was employed, utilizing semi-structured interviews to gather in-depth insights from participants. Individuals from various marginalized communities, including ethnic minorities, low-income groups, and those with disabilities, were purposefully sampled. Data collection continued until theoretical saturation was achieved, with interviews being transcribed verbatim and analyzed using NVivo software. A thematic analysis approach was used to identify and categorize recurring themes and patterns in the data. Results: The study identified three main themes: social inclusion, health and well-being, and personal development and growth. Participants reported that sports participation significantly enhanced their social networks, community cohesion, and broke down social barriers, fostering a sense of belonging and empowerment. Physical health benefits included improved fitness and reduced chronic diseases, while mental health improvements encompassed stress relief, anxiety reduction, and mood enhancement. Sports also facilitated personal development by fostering leadership skills, teamwork, and providing educational and career opportunities. Conclusion: Sports play a crucial role in promoting social inclusion and health within marginalized communities. The findings underscore the multifaceted benefits of sports participation, including enhanced social connections, improved physical and mental health, and personal empowerment. The study suggests that increasing access to inclusive sports programs and addressing barriers to participation can further amplify these benefits.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".