Exploring Student Voices: An Analysis of Student Feedback from RISE for Health’s 2023 Learning Sessions.
Bibliographic record
Abstract
Background: The immigrant population faces unique barriers and do not experience the same high standard of health as the non-immigrant population in Canada (1). Youth engagement programs have been shown to strengthen community well-being (2). The RISE for Health program develops health sessions tailored to immigrant and refugee youth needs. These sessions aim to empower youth to become health advocates within their communities. Purpose: Analyze the RISE for Health participants’ feedback following the 2023 learning sessions and assess how participants aim to apply what they learned to their lives and communities. Methods: Immigrant and refugee high school youth participated in a series of RISE for Health learning sessions from July to August of 2023. Following each session, students completed a post-session survey that asked students to provide a rating, suggestions for improvement, and how they will apply what they learned. Students' responses regarding how they planned to apply their newly gained knowledge were implemented into our analysis to assess knowledge uptake and application following participation in the RISE sessions. Results: Analysis of the post-session surveys indicated that the majority of the participants aimed to apply their newly gained knowledge by informing family members, peers, and their broader community. Participants also highlighted the significance of improving community ties and a desire to become active advocates within their communities to encourage positive, healthy change. Moreover, many participants shared that the sessions motivated them to advance their own health (i.e., improve coping mechanisms, setting healthy goals, etc.). Conclusion: The RISE for Health Program aims to advance newcomer health outcomes by empowering youth to become active health leaders within their communities. The findings of this analysis suggest that students are completing the sessions with the goal of applying their newly gained knowledge and skills to their communities. Youth engagement within communities is critical to strengthening the capacities and overall standard of health in immigrant communities. References: (1) Ravichandiran, N., Mathews, M. & Ryan, B.L. Utilization of healthcare by immigrants in Canada: a cross-sectional analysis of the Canadian Community Health Survey. BMC Prim. Care 23, 69 (2022). https://doi.org/10.1186/s12875-022-01682-2 (2) Sprague Martinez L, Pufall Jones E, Connolly Ba N. From Consultation to Shared Decision-Making: Youth Engagement Strategies for Promoting School and Community Wellbeing. J Sch Health. 2020;90(12):976-984. doi:10.1111/josh.12960
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".