Developing a sense of belonging through sport: A meta-synthesis of qualitative research conducted with refugee sport participants
Bibliographic record
Abstract
The number of refugees worldwide exceeded 20 million people for the first time in 2018 (UNHCR, 2019). Sport participation has been posited as a cost-effective way to promote integration between forced migrants (i.e., refugees and asylum seekers) and the host culture they are situated within (Government of Canada, 2012). However, simply enrolling forced migrants in sport programs may not convey the benefits inherently attributed to sport participation (Ryba et al., 2017). Qualitative researchers have worked to bring a deeper understanding to the intricacies of employing sport participation to foster positive integration and a sense of belonging for forced migrants (e.g., Schinke et al., 2019). Seeking to bring together the diverse and varying findings from these researchers we conducted an interpretive meta-synthesis. Through a systematic search, we included 26 articles published from 1990 to 2018 focused on the participation of forced migrants in sport and/or physical activity programs in their host country. The focus of these articles was found to be twofold: (a) barriers/facilitators to participation and (b) approaches to integration through sport and physical activity. Further, based on our own interpretations of the synthesized knowledge, two themes were developed that should be the focus of future research: (1) the impact of the journey to their host country on forced migrants and (2) the role of power in determining forced migrants' sport and physical activity involvement. These findings help to transform our understanding of how sport may or may not promote the integration of those seeking refuge in a host country.Acknowledgments: SSHRC
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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 | 0.068 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".