Enhancing Class Culture: Assessing and Improving the Impact of the "Thriving Together" Workshop for Dalhousie Medical Students
Bibliographic record
Abstract
Introduction Collaboration and collegiality in medical school benefit students' experiences and contribute to improved patient care. Learning environments have the potential to foster competition and discourage collaboration. Thriving Together was created to address class dynamics and culture early in medical training. Objective The objective of the study is to thoroughly evaluate the Thriving Together Workshop. Methods The Thriving Together workshop, led by upper-year students, comprises a presentation on class culture, anonymous polling, and small-group case-based exercises. It concludes with a large-group discussion. Pre- and post-workshop survey results were collected via Opinio software. A basic statistical and thematic analysis was conducted to identify response themes. Results The post-workshop survey response rate was 29 out of 41 attendees (70.7%) in 2022 and 20 out of 55 attendees (36.4%) in 2023. Forty-eight (96.6%) respondents would recommend the workshop to next year's medical cohort, and 44 (89.8%) were interested in a follow-up workshop. Qualitative comments were positive, with feedback focused on attendance, group randomization, and the need for formal resources and post-workshop follow-up. Conclusion The Thriving Together workshop has a positive impact on class culture as evidenced by voluntary attendance and positive survey responses. Strategies to improve attendance will be implemented for upcoming sessions and will focus on refining the workshop to encourage inter-group interactions. In addition, formal resources will be provided to those interested. These adjustments aim to sustain the positive impact of the Thriving Together initiative on medical school culture.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".