Creating Better Physical Activity Opportunities for Newcomer Children and Youth: Overview of Activities to Strengthen Community-Academic Partnerships.
Bibliographic record
Abstract
Background: Newcomer children and youth have been found to have physical activity (PA) deficits when compared to Canadian-born peers. While the underlying reasons behind this is complex, the extant literature suggest that young newcomers to Canada face multiple unique barriers. The development of Community-Academic Partnerships (CAP) is considered vital towards creating meaningful system-level changes to address issues of access. The purpose of this presentation is to provide an overview (from both academic and community partner perspectives) of two innovative CAP initiatives aimed at creating better PA opportunities for newcomer children and youth. Methods/Approaches: (1) Using a participatory approach, we conducted a CIHR-funded summit that brought together a multidisciplinary and multisectoral group of academics and community leaders to identify needs and opportunities. (2) Using a mixed-methods approach, we evaluated the impact of an 8-week pilot physical literacy-based program called the (IPLAY) program – conducted in partnership with WinSport and Calgary Catholic Immigration Society. Results: The summit brought together a group of academics across Health Sciences, Psychology, Sociology, Kinesiology, and Education to hear the needs from community organizations looking to develop active programming for newcomers, identifying the need for more formative research, efficacy trials, and integrated knowledge translation strategies. Findings from the pilot IPLAY program with 36 refugee youths illustrates the potential positive impact of community programs targeting newcomer youths; although the process highlighted the saliency of community partnerships in recruitment and need for cultural responsiveness in program delivery. Conclusions: CAP is critical to address the PA deficits for newcomer children and youth.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".