The Wellness Ambassador Program: A Student-Led Initiative to Promote Wellness and Connection Among Trainees During and Beyond the COVID-19 Pandemic
Bibliographic record
Abstract
To the Editor: Medical student well-being is closely associated with the level of social support they can access. Students with inadequate social support are at greater risk of experiencing depression and burnout. 1 Furthermore, social support is positively correlated with empathy, a core competency for medical trainees. 2 Social isolation has become a hallmark of many students’ medical school experience since schools in Canada moved to virtual learning due to the COVID-19 pandemic. Students entering medical school in the fall of 2020 were deprived of in-person opportunities to connect with classmates, experiences that had been fundamental to developing a sense of community and belonging among students in previous years. In the 2020–2021 academic year, 54.0% of first-year medical students at the University of Toronto reported a level of social isolation that contributed to their daily stress. The same students also reported feelings of disconnectedness and lack of social support. To address these challenges, we developed the Wellness Ambassador (WA) program. This involved training a group of preclerkship medical students at the University of Toronto to navigate resources related to equity, mental health, diversity and inclusion, sexual violence prevention and support, and crisis identification. These students became WAs and served as resources for their peers. The program allowed students to anonymously access WAs for support in resource navigation. In addition, WAs created weekly social media posts to highlight resources and ways to cope with stress and cultivate wellness. The program encouraged social connection with a Virtual Med Lounge series, a casual and inclusive platform for students to connect with their peers online. Students who attended these sessions reported feeling connected and supported. Inadequate social support for medical students is detrimental to their mental health and academic performance. The COVID-19 pandemic has exacerbated the need for social connection. Through initiatives like the WA program, we are creating student connections and improving access to formal wellness resources during and beyond the COVID-19 pandemic. Acknowledgments: The authors thank the members of the 2020–2021 Wellness Ambassador team, including Eyram Asem, Michal Coret, Amal Ga’al, Shamini Vijaya Kumar, Danny Ma, Fahmeeda Murtaza, Christie Tan, Sam Wier, and Faiyaz Zaman, for their outstanding work. Furthermore, they thank Caroline Park and Anna Chen, Student Health Initiatives and Education general coordinators in 2020–2021 and 2019–2020, respectively, for their leadership and continued support of the Wellness Ambassador program as well as Dr. Tony Pignatiello and Shayna Kulman-Lipsey, both of the Office of Health Professions Student Affairs, for their generous support and commitment to student wellness.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".