Use of Social Media and Community Outreach for Immigrant Youth Recruitment during COVID-19
Bibliographic record
Abstract
Background: When starting any program, it is essential to have an efficient approach of recruitment among your target population. We outline below the approach employed for recruiting immigrant and refugee teens between the ages of 14 and 18 for the Refugee and Immigrant SelfEmpowerment (RISE) for Health and Wellness summer program. Methods: We planned for a multi-pronged approach as we anticipated that this approach would be able to touch the largest number of youths, thus increasing our likelihood of having participants. As such, we aimed at placing our recruitment materials in schools, community activities, and social media. We expected that these would be the highest viewed areas during Covid-19. In schools, posters were placed within hallways and were shared by RISE youth volunteers. Social media was employed through the use of weekly Instagram posts regarding health matters and through triweekly stories endorsing the program. Finally, RISE posters had been sent to community and cultural groups throughout Calgary for their distribution. Results: More than 20 students applied to participate at the beginning of the year whereas only 9 completed the program indicating that some methods were more effective than others. The specific factors are yet to be known and will be found through future focus groups. Conclusion: Feedback from youth regarding the approach is still needed to understand the most effective approaches used. However, it should be noted that a number of other approaches, such as promotion through a central website and parental engagement, should be taken in the future.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".