RISE for Health and Wellness: Engaging Immigrant Youth as Community Champions to Develop a Summer Program Curriculum Towards Community Capacity
Bibliographic record
Abstract
Community-capacity building is important for improving the health of minority communities (Labonte, 2002). Youth engagement programs focused on health-wellness and skill development have been shown to increase immigrant youth interests in improving the health of their communities (Larson and Angus, 2019; McLean et al., 2018). However, to our knowledge the communities themselves are seldomly involved in developing these programs. As such, a summer program about health was co-created with immigrant youth acting as “community champions”, to ensure the program was developed to fit youth needs. Youth community champions were engaged from previous program sessions and were involved in all steps of curriculum development. In cooperation with a team of undergraduate and medical students from the University of Calgary, activities related to three areas of health and wellness were developed: (a) physical health, (b) mental health, and (c) social wellbeing. After determining topics, experts in topic fields were contacted to assist with facilitating program sessions. The product of the partnership between youth champions, students, and community partners were twelve summer sessions consisting of a combination of lectures from community experts, and activities designed by team leads that incorporated skill development in areas such as leadership, communication, and critical thinking. A summer long research assignment was added to encourage participants to reflect on what they’ve experienced within their communities. The involvement of immigrant youth in the development of the sessions is expected to lead to greater youth engagement within immigrant communities, so that they may improve the capacities of their communities.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".