Wellbeing Convene during COVID-19: A pilot intervention for improving wellbeing and social connectedness for staff, students, residents, and faculty
Bibliographic record
Abstract
Abstract Background: Canada is facing its worst crisis among healthcare workers in recent healthcare history. Anxiety, depression, suicidal ideation, and severe burnout are higher than before the COVID-19 pandemic. University Faculties of Medicine (FoMs) are vital to healthcare systems. Not only are they responsible for training personnel, but clinicians and staff from FoMs often work directly within healthcare systems. FoMs include students, staff, residents, faculty members, residents, researchers, and others, many experiencing higher stress levels due to pandemic tensions. Most FoMs emphasize cognitive and psychomotor learning needs. On the other hand, affective learning needs are not as well addressed within most FoMs. Finding innovative means to ameliorate mental and emotional health status, particularly at this critical juncture, will improve health and wellness, productivity, and retention. This article discusses a pilot program, Wellbeing Convene during COVID-19, in a Canadian FoM, which aimed to (1) provide staff, faculty, residents, and students with a toolkit for greater wellbeing and (2) build a sense of community during isolating times. Results: Participants found the program beneficial in both regards. We recommend that these kinds of programs be permanently available to all members in FoMs, at no cost. Wellness programs alone, however, will not solve the root causes of mental and emotional stress, often based on concerns related to finances, hierarchical workplace structures, and nature of the work itself, among other factors. Conclusion: Addressing the mental and emotional health of people in FoMs is vital to improving productivity and reducing stress of FoMs, healthcare professionals, and, ultimately, patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".