Developing Resources for Staff and Adapting Programing During COVID-19 at Fred Victor
Bibliographic record
Abstract
Fred Victor is an organization that supports those experiencing poverty and homelessness in Toronto. As a practicum student in the Health Promotions department at Fred Victor, I gained experience working on health promotion projects and was able to work directly with the community. Throughout the practicum, I worked on several projects to adapt Fred Victor’s services during COVID-19. First, I worked to develop a resilience toolkit for Fred Victor staff. COVID-19 has led to higher levels of stress. This prompted Fred Victor to develop tools to support their staff. I designed a toolkit that instructs managers on how to promote resilience in their supervision sessions and team meetings. This toolkit provided information on what resilience is, as well as practical actions that managers can take to promote resilience in staff. This project involved knowledge translation to convey the research on resilience to Fred Victor staff in an accessible way. Additionally, I worked to support the development of online peer support groups. Typically, Fred Victor runs weekly in-person peer support groups for community members. However, due to COVID-19, these groups had to move to an online format. I helped facilitate this transition by developing a guide for facilitating online group programming. This guide included information on the best platforms to run online programming, how to create a safety agreement, and best practices for facilitating the group. I then conducted outreach to community members to ask for their input on the format and content of the groups. These projects are important to public health as they work to meet the public health goal to improve quality of life by promoting and encouraging healthy behaviours. These projects played an important role in promoting the health of Fred Victor staff and clients during COVID-19 by providing them with support and tools to manage their mental health.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".