Development of Professional Relationships in RISE for Health and Wellness.
Bibliographic record
Abstract
Background: Community-capacity building is an important tenet required to improve the health of minority communities. One avenue of improving the health of minority communities is by developing champions of health who are capable of learning and disseminating information along their social circles. Through this program we have noticed the development of professional relationships between session coordinators, guest speakers and participant recruitment aides. Methods: Following every session, participants provided feedback on what they have learned, session improvement ideas, and how participants can utilize their novel knowledge. In addition, focus groups pertaining to areas of interest for future programs were conducted to inform speakers about points of interest for students. New speakers were sourced through a combination of methods including insights gathered from focus groups and post-session reflections as well as conducting thorough and meticulous searches. These inputs helped us identify and assess the suitability of the speakers for future sessions. Our efforts encompass both in person and online meetings in which we communicated the needs and expectations of the participants and the program with the potential speakers. The potential speakers also evaluated the program’s alignment with their interests and communicated their specific requirements. Novel techniques such as in-class presentations, Instagram posts, and standardized flyers were also utilized to recruit both participants and speakers. Results: Since 2017, we have continuously worked with physicians, researchers, high schools, and other youth engagement programs to develop a comprehensive health-based syllabus for high school students. Focus groups following every program and post-session surveys have resulted in continuous feedback and areas of improvement for speakers and us. One observable trend is an increase in positive feedback with returning speakers from previous years. However, we have noticed that the expectations of students continue to evolve annually due to the variability in backgrounds of participants. Due to consistent meetings and feedback aimed at improving the provided sessions, long-lasting relationships have been developed resulting in continued annual involvement of professionals with participants and volunteers. Conclusion: We observed an increase in rapports with individuals and organizations across Calgary through our grass-root initiative. Every year, students provide beneficial feedback while also indicating that sessions are more beneficial than years before. We continue to develop novel relationships while continuing our current relationships so that the best information may be presented in the most manageable way for students. We aim to follow up with speakers directly in the nearby future to see how our program has changed their own practices.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".