Check-In: An Educational Activity to Address Well-Being and Burnout among Pharmacy Students
Bibliographic record
Abstract
Background: Chronic workplace stress that has not been adequately managed can result in burnout. Healthcare providers; including pharmacists, may be particularly susceptible to this phenomenon, prompting the School of Pharmacy at the University of Waterloo to develop an active-learning activity to teach and reflect on healthcare provider burnout, called Check-In. Methods: Check-In was comprised of a 20 min online lecture on healthcare provider burnout, two pre-readings that highlighted burnout among physicians, and an optional one-on-one session between individual students and a faculty or staff member. A reflection guide was also shared among students and facilitators where students had to rate their current mental health on a 10-point scale and reflect on questions focusing on energy expenditure, self-care, and self-compassion within the past, present, and future. Results: Check-In was rewarding and overall positive for students and faculty. The personal connection with members from the school and the strategic timing of the activity within the curriculum notably contributed to the success of the activity. The short duration of individual sessions was the key criticism of the activity. Further research at the University of Waterloo School of Pharmacy will be explored to assess the long-term impact of Check-In on student well-being.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".